Number of hospitals and status (low mortality, non-outlier, high mortality) according to the various methods; the ratio of observed to expected number of deaths (OE), variance corrected OE (OE-Faris), logistic regression (LR), bias corrected LR (LR-Firth), trimmed mean variants (LR trimmed and LR-Firth trimmed).

<p>Fleiss’ kappa for agreement across methods.</p

Observed to expected or logistic regression to identify hospitals with high or low 30-day mortality?

<div><p>Introduction</p><p>A common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied.</p><p>Materials and methods</p><p>To study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012–2014 were analysed.</p><p>Results</p><p>None of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals.</p><p>Conclusion</p><p>We recommend, on the balance, LR-Firth 10% or 25% trimmed for detection of both low and high mortality outliers.</p></div

Hospital and patient characteristics, data from Norwegian hospitals 2012–2014.

<p>Hospital and patient characteristics, data from Norwegian hospitals 2012–2014.</p

Results of the simulation study. Level of significance and power for the different methods for one-sided tests at 0.05 nominal level per hospital volume and outlier category, aggregated over all scenarios A-J, number of hospitals compared, and the three mortality sets.

<p>OE = the ratio of observed to expected number of deaths; OE-Faris = variance corrected OE; LR = logistic regression using maximum likelihood; LR-Firth = LR with bias correction; LR 5%, 10% and 25% trim. = trimmed mean variants of LR; LR-Firth 5%, 10%, and 25% trim. = trimmed mean variants of LR-Firth; excl. 0-deaths = excluding hospitals with no deaths.</p

Design of simulation scenarios: Number of hospitals according to hospital volume (number of patients) and outlier status (low mortality, non-outlier, high mortality).

<p>Design of simulation scenarios: Number of hospitals according to hospital volume (number of patients) and outlier status (low mortality, non-outlier, high mortality).</p

Sampling probabilities and input regression estimates for simulation scenarios, logistic scale.

<p><i>μ</i>_<i>low</i>, <i>μ</i>_{<i>non</i>−<i>outlier</i>}, and <i>μ</i>_<i>high</i> are the hospital specific mortality effects for low mortality outliers, non-outliers, and high mortality outliers. <i>γ</i>_<i>sex</i> and <i>γ</i>_<i>age</i> are the regression coefficients for sex and age, respectively.</p

Shape Information in Repeated Glucose Curves during Pregnancy Provided Significant Physiological Information for Neonatal Outcomes

<div><p>Objective</p><p>To use multilevel functional principal component analysis to exploit the information inherent in the shape of longitudinally sampled glucose curves during pregnancy, and to analyse the impact of glucose curve characteristics on neonatal birth weight, percentage fat and cord blood C-peptide.</p><p>Study Design and Setting</p><p>A cohort study of healthy, pregnant women (n = 884). They underwent two oral glucose tolerance tests (gestational weeks 14–16 and 30–32), which gave two glucose curves per woman.</p><p>Results</p><p>Glucose values were higher, and peaked later in third trimester than in early pregnancy. The curve characteristic “general glucose level” accounted for 91% of the variation across visits, and 72% within visits. The curve characteristics “timing of postprandial peak”, and “oscillating glucose levels” accounted for a larger part of the variation within visits (15% and 8%), than across visits (7% and <2%). A late postprandial peak during pregnancy, and high general glucose levels in third trimester had significant, positive effects on birth weight (p<0.05). Generally high glucose levels during pregnancy had a significant, positive impact on neonatal percentage fat (p = 0.04). High general glucose level in third trimester had a significant, positive impact on cord blood C-peptide (p = 0.004).</p><p>Conclusion</p><p>Shape information in entire OGTT curves provides significant physiological information of importance for several outcomes, and may contribute to the understanding of the metabolic changes during pregnancy.</p></div

Regression analyses.

<p>Results from univariable and multivariable regression analyses, with birth weight, neonatal percentage fat, or C-peptide in cord blood as response variables.</p><p>*Multivariable analyses included all glucose variables except FPC1^14–16, due to colinearity diagnostics. Variable selection was done by Akaike's information criterion.</p

Flow chart.

<p>Flow chart.</p

Examples of individual curves and corresponding scores.

<p>The upper, left plot shows the individual glucose curves from 6 women at gestational weeks 14–16, and the upper, right plot shows the glucose curves from the same 6 women at gestational weeks 30–32. The lower plot shows the FPC scores for the same 6 women. The grey curves in the upper plots are the mean glucose curves at gestational weeks 14–16 (left) and 30–32 (right). Correspondingly, the grey line in the lower plot is zero.</p