A nonparametric fiducial interval for the Youden index in multi-state diagnostic settings

Optimized machine learning approaches to combine surface-enhanced Raman scattering and infrared data for trace detection of xylazine in illicit opioids

Infrared absorption spectroscopy and surface-enhanced Raman spectroscopy were integrated into three data fusion strategies-hybrid (concatenated spectra), mid-level (extracted features from both datasets) and high-level (fusion of predictions from both models)-to enhance the predictive accuracy for xylazine detection in illicit opioid samples. Three chemometric approaches-random forest, support vector machine, and k-nearest neighbor algorithms-were employed and optimized using a 5-fold cross-validation grid search for all fusion strategies. Validation results identified the random forest classifier as the optimal model for all fusion strategies, achieving high sensitivity (88% for hybrid, 92% for mid-level, and 96% for high-level) and specificity (88% for hybrid, mid-level, and high-level). The enhanced performance of the high-level fusion approach (F1 score of 92%) is demonstrated, effectively leveraging the surface-enhanced Raman data with a 90% voting weight, without compromising prediction accuracy (92%) when combined with infrared spectral data. This highlights the viability of a multi-instrument approach using data fusion and random forest classification to improve the detection of various components in complex opioid samples in a point-of-care setting.

A fiducial-based confidence interval for the linear combination of multinomial probabilities

Across a broad set of applications, system outcomes may be summarized as probabilities in confusion or contingency tables. In settings with more than two outcomes (e.g., stages of cancer), these outcomes represent multinomial experiments. Measures to summarize system performance have been presented as linear combinations of the resulting multinomial probabilities. Statistical inference on the linear combination of multinomial probabilities has been focused on large-sample and parametric settings and not small-sample settings. Such inference is valuable, however, especially in settings such as those resulting from pilot or low-cost studies. To address this gap, we leverage the fiducial approach to derive confidence intervals around the linear combination of multinomial parameters with desirable frequentist properties. One of the original arguments against the fiducial approach was its inability to extend to multiparameter settings. Therefore, the great novelty of this work is both the derived interval and the logical framework for applying the fiducial approach in multiparameter settings. Through simulation, we demonstrate that the proposed method maintains a minimum coverage of1 - α $1 - \alpha$ , unlike the bootstrap and large-sample methods, at comparable interval lengths. Finally, we illustrate its use in a medical problem of selecting classifiers for diagnosing chronic allograph nephropathy in postkidney transplant patients.

Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes

BACKGROUND

Despite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high-income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle-income country (MIC) context.

METHODS

A 5-step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in-hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer-based score that reliably predicts in-hospital death in the 2012 ACS-NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement.

RESULTS

Following step one-three above, the 13 point Codman's score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions ≥4 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score ≥3 (3) and operative procedure (1-3). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in-hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths.

CONCLUSION

We have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision-making and (2) benchmarking the quality of surgical care in MIC's.