Citations

Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.

Background: Hospitals’ risk-standardized mortality rates and outlier status (significantly higher/lower rates) are reported by the Centers for Medicare and Medicaid Services (CMS) for acute myocardial infarction (AMI) patients using Medicare claims data. New York now has AMI claims data with blood pressure and heart rate added. Objective: The objective of this study was to see whether the appended database yields different hospital assessments than standard claims data. Methods: New York State clinically appended claims data for AMI were used to create 2 different risk models based on CMS methods: 1 with and 1 without the added clinical data. Model discrimination was compared, and differences between the models in hospital outlier status and tertile status were examined. Results: Mean arterial pressure and heart rate were both significant predictors of mortality in the clinically appended model. The C statistic for the model with the clinical variables added was significantly higher (0.803 vs. 0.773, P<0.001). The model without clinical variables identified 10 low outliers and all of them were percutaneous coronary intervention hospitals. When clinical variables were included in the model, only 6 of those 10 hospitals were low outliers, but there were 2 new low outliers. The model without clinical variables had only 3 high outliers, and the model with clinical variables included identified 2 new high outliers. Conclusion: Appending even a small number of clinical data elements to administrative data resulted in a difference in the assessment of hospital mortality outliers for AMI. The strategy of adding limited but important clinical data elements to administrative datasets should be considered when evaluating hospital quality for procedures and other medical conditions.

Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.

Background.Noninvasive prenatal screening (NIPS) is revolutionizing prenatal screening as a result of its increased sensitivity, specificity. NIPS analyzes cell-free fetal DNA (cffDNA) circulating in maternal plasma to detect fetal chromosome abnormalities. However, cffDNA originates from apoptotic placental trophoblast; therefore cffDNA is not always representative of the fetus. Although the published data for NIPS testing states that the current technique ensures high sensitivity and specificity for aneuploidy detection, false positives are possible due to isolated placental mosaicism, vanishing twin or cotwin demise, and maternal chromosome abnormalities or malignancy.Results.We report a case of false negative cell-free DNA (cfDNA) screening due to fetoplacental mosaicism. An infant male with negative cfDNA screening result was born with multiple congenital abnormalities. Postnatal chromosome and FISH studies on a blood specimen revealed trisomy 13 in 20/20 metaphases and 100% interphase nuclei, respectively. FISH analysis on tissues collected after delivery revealed extraembryonic mosaicism.Conclusions.Extraembryonic tissue mosaicism is likely responsible for the false negative cfDNA screening result. This case illustrates that a negative result does not rule out the possibility of a fetus affected with a trisomy, as cffDNA is derived from the placenta and therefore may not accurately represent the fetal genetic information.

SummaryBackgroundAzathioprine and mercaptopurine have a pivotal role in the treatment of inflammatory bowel disease (IBD). However, because of their complex metabolism and potential toxicities, optimal use of biomarkers to predict adverse effects and therapeutic response is paramount.AimTo provide a comprehensive review focused on pharmacogenetics and pharmacokinetics for safe and effective thiopurine therapy inIBD.MethodsA literature search up to July 2015 was performed in PubMed using a combination of relevant MeSHterms.ResultsPre‐treatment thiopurine S‐methyltransferase typing plus measurement of 6‐tioguanine nucleotides and 6‐methylmercaptopurine ribonucleotides levels during treatment have emerged with key roles in facilitating safe and effective thiopurine therapy. Optimal use of these tools has been shown to reduce the risk of adverse effects by 3‐7%, and to improve efficacy by 15–30%. For the introduction of aldehyde oxidase (AOX) into clinical practice, the association betweenAOXactivity andAZAdose requirements should be positively confirmed. Inosine triphosphatase assessment associated with adverse effects also shows promise. Nucleoside diphosphate‐linked moiety X‐type motif 15 variants have been shown to predict myelotoxicity on thiopurines in East Asian patients. However, the impact of assessments of xanthine oxidase, glutathione S‐transferase, hypoxanthine guanine phosphoribosyltransferase and inosine monophosphate dehydrogenase appears too low to favour incorporation into clinical practice.ConclusionsMeasurement of thiopurine‐related enzymes and metabolites reduces the risk of adverse effects and improves efficacy, and should be considered part of standard management. However, this approach will not predict or avoid all adverse effects, and careful clinical and laboratory monitoring of patients receiving thiopurines remains essential.

Neonatal critical aortic stenosis is life threatening and requires intervention shortly after birth. Early percutaneous aortic valvuloplasty is generally considered to be an effective way to obtain reliable prograde flow. We describe the integrated use of hybrid, open surgical, and single ventricle rehabilitation techniques to successfully treat a neonate with borderline left-sided cardiac structures, who sustained a complication from balloon aortic valvuloplasty.

ABSTRACT The health care-associated pneumonia (HCAP) criteria have a limited ability to predict pneumonia caused by drug-resistant bacteria and favor the overutilization of broad-spectrum antibiotics. We aimed to derive and validate a clinical prediction score with an improved ability to predict the risk of pneumonia due to drug-resistant pathogens compared to that of HCAP criteria. A derivation cohort of 200 microbiologically confirmed pneumonia cases in 2011 and 2012 was identified retrospectively. Risk factors for pneumonia due to drug-resistant pathogens were evaluated by logistic regression, and a novel prediction score (the drug resistance in pneumonia [DRIP] score) was derived. The score was then validated in a prospective, observational cohort of 200 microbiologically confirmed cases of pneumonia at four U.S. centers in 2013 and 2014. The DRIP score (area under the receiver operator curve [AUROC], 0.88 [95% confidence interval {CI}, 0.82 to 0.93]) performed significantly better ( P = 0.02) than the HCAP criteria (AUROC, 0.72 [95% CI, 0.64 to 0.79]). At a threshold of ≥4 points, the DRIP score demonstrated a sensitivity of 0.82 (95% CI, 0.67 to 0.88), a specificity of 0.81 (95% CI, 0.73 to 0.87), a positive predictive value (PPV) of 0.68 (95% CI, 0.56 to 0.78), and a negative predictive value (NPV) of 0.90 (95% CI, 0.81 to 0.93). By comparison, the performance of HCAP criteria was less favorable: sensitivity was 0.79 (95% CI, 0.67 to 0.88), specificity was 0.65 (95% CI, 0.56 to 0.73), PPV was 0.53 (95% CI, 0.42 to 0.63), and NPV was 0.86 (95% CI, 0.77 to 0.92). The overall accuracy of the HCAP criteria was 69.5% (95% CI, 62.5 to 75.7%), whereas that of the DRIP score was 81.5% (95% CI, 74.2 to 85.6%) ( P = 0.005). Unnecessary extended-spectrum antibiotics were recommended 46% less frequently by applying the DRIP score (25/200, 12.5%) than by use of HCAP criteria (47/200, 23.5%) ( P = 0.004), without increasing the rate at which inadequate treatment recommendations were made. The DRIP score was more predictive of the risk of pneumonia due to drug-resistant pathogens than HCAP criteria and may have the potential to decrease antibiotic overutilization in patients with pneumonia. Validation in larger cohorts of patients with pneumonia due to all causes is necessary.