Hospital profiling using Bayesian decision theory.

For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non-outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub.

Mixed modeling for irregularly sampled and correlated functional data: Speech science applications.

The speech sciences often employ complex experimental designs requiring models with multiple covariates and crossed random effects. For curve-like data such as time-varying signals, single-time-point feature extraction is commonly used as data reduction technique to make the data amenable to statistical hypothesis testing, thereby discarding a wealth of information. The present paper discusses the application of functional linear mixed models, a functional analogue to linear mixed models. This type of model allows for the holistic evaluation of curve dynamics for data with complex correlation structures due to repeated measures on subjects and stimulus items. The nonparametric, spline-based estimation technique allows for correlated functional data to be observed irregularly, or even sparsely. This means that information on variation in the temporal domain is preserved. Functional principal component analysis is used for parsimonious data representation and variance decomposition. The basic functionality and usage of the model is illustrated based on several case studies with different data types and experimental designs. The statistical method is broadly applicable to any types of data that consist of groups of curves, whether they are articulatory or acoustic time series data, or generally any types of data suitably modeled based on penalized splines.

Nutritional screening for risk prediction in patients scheduled for extra-abdominal surgery.

OBJECTIVE: The Nutritional Risk Screening-2002 (NRS-2000) is currently recommended by the European Society of Parenteral and Enteral Nutrition as a screening tool in hospitalized patients. However, for preoperative risk prediction, the usefulness of this tool is uncertain and may depend on the type of surgical disease. The present study investigated the relative prognostic importance of the NRS-2002 and of established medical and surgical predictors for postoperative complications in patients scheduled for non-abdominal procedures. METHODS: In this prospective observational study, we enrolled 581 patients scheduled for elective non-abdominal surgery. Data were collected on nutritional variables (body mass index, weight loss, and food intake), age, gender, type of surgery, extent of surgery, underlying disease, American Society of Anesthesiologists class, and comorbidity. We also evaluated a modification of the NRS-2002 (ordinal graduation according to <2 or ≥2 points) and the importance of individual parameter values. Relative complication rates were calculated with generalized linear models and cumulative proportional odds models. RESULTS: Forty-four patients (7.6%) sustained at least one postoperative complication. The frequency of this event increased significantly with a higher NRS-2002 score. However, the model that performed the best (sensitivity 81.8%, specificity 78.6%) included the modified NRS-2002 graduation (<2 or ≥2 points) and other factors such as American Society of Anesthesiologists class, the duration of the procedure, and the need for red blood cell transfusion. CONCLUSION: In surgical patients with non-abdominal diseases, a modified NRS-2002 classification may be required to preoperatively identify patients at a high nutritional risk. The NRS-2002 alone is insufficient to precisely predict complications.