Measures for evaluation of prognostic improvement under multivariate normality for nested and nonnested models

Predicting risk of peripartum blood transfusion during vaginal and cesarean delivery: A risk prediction model

OBJECTIVE

The objective of this study is to develop a model that will help predict the risk of blood transfusion using information available prior to delivery.

STUDY DESIGN

The study is a secondary analysis of the Consortium on Safe Labor registry. Women who had a delivery from 2002 to 2008 were included. Pre-delivery variables that had significant associations with transfusion were included in a multivariable logistic regression model predicting transfusion. The prediction model was internally validated using randomly selected samples from the same population of women.

RESULTS

Of 156,572 deliveries, 5,463 deliveries (3.5%) required transfusion. Women who had deliveries requiring transfusion were more likely to have a number of comorbidities such as preeclampsia (6.3% versus 4.1%, OR 1.21, 95% CI 1.08-1.36), placenta previa (1.8% versus 0.4%, OR 4.11, 95% CI 3.25-5.21) and anemia (10.6% versus 5.4%, OR 1.30, 95% CI 1.21-1.41). Transfusion was least likely to occur in university teaching hospitals compared to community hospitals. The c statistic was 0.71 (95% CI 0.70-0.72) in the derivation sample. The most salient predictors of transfusion included type of hospital, placenta previa, multiple gestations, diabetes mellitus, anemia, asthma, previous births, preeclampsia, type of insurance, age, gestational age, and vertex presentation. The model was well-calibrated and showed strong internal validation.

CONCLUSION

The model identified independent risk factors that can help predict the risk of transfusion prior to delivery. If externally validated in another dataset, this model can assist health care professionals counsel patients and prepare facilities/resources to reduce maternal morbidity.

Systematic review and meta-analysis of mortality risk prediction models in adult cardiac surgery

OBJECTIVES

The most used mortality risk prediction models in cardiac surgery are the European System for Cardiac Operative Risk Evaluation (ES) and Society of Thoracic Surgeons (STS) score. There is no agreement on which score should be considered more accurate nor which score should be utilized in each population subgroup. We sought to provide a thorough quantitative assessment of these 2 models.

METHODS

We performed a systematic literature review and captured information on discrimination, as quantified by the area under the receiver operator curve (AUC), and calibration, as quantified by the ratio of observed-to-expected mortality (O:E). We performed random effects meta-analysis of the performance of the individual models as well as pairwise comparisons and subgroup analysis by procedure type, time and continent.

RESULTS

The ES2 {AUC 0.783 [95% confidence interval (CI) 0.765–0.800]; O:E 1.102 (95% CI 0.943–1.289)} and STS [AUC 0.757 (95% CI 0.727–0.785); O:E 1.111 (95% CI 0.853–1.447)] showed good overall discrimination and calibration. There was no significant difference in the discrimination of the 2 models (difference in AUC −0.016; 95% CI −0.034 to −0.002; P = 0.09). However, the calibration of ES2 showed significant geographical variations (P < 0.001) and a trend towards miscalibration with time (P=0.057). This was not seen with STS.

CONCLUSIONS

ES2 and STS are reliable predictors of short-term mortality following adult cardiac surgery in the populations from which they were derived. STS may have broader applications when comparing outcomes across continents as compared to ES2.

REGISTRATION

Prospero (https://www.crd.york.ac.uk/PROSPERO/) CRD42020220983.

Statistical inference for decision curve analysis, with applications to cataract diagnosis

Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel complement as it incorporates a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models. The preference of a patient or a policy-maker is formulated statistically as the underlying threshold probability, above which the patient would choose to be treated. Net benefit is then calculated for possible threshold probability, which places benefits and harms on the same scale. We consider the inference problems for DCA in this paper. Interval estimation procedure and inference methodology are provided after we derive the relevant asymptotic properties. Our formulation can accommodate the classification problems with multiple categories. We carry out numerical studies to assess the performance of the proposed methods. An eye disease dataset is analyzed to illustrate our proposals.