Using logistic regression models to investigate the effects of high-sensitivity cardiac troponin T confounders on ruling in acute myocardial infarction.

OBJECTIVES: Confounding factors, including sex, age, and renal dysfunction, affect high-sensitivity cardiac troponin T (hs-cTnT) concentrations and the acute myocardial infarction (AMI) diagnosis. This study assessed the effects of these confounders through logistic regression models and evaluated the diagnostic performance of an optimized, integrated prediction model. METHODS: This retrospective study included a primary derivation cohort of 18,022 emergency department (ED) patients at a US medical center and a validation cohort of 890 ED patients at a Canadian medical center. Hs-cTnT was measured with 0/3 h sampling. The primary outcome was index AMI diagnosis. Logistic regression models were optimized to predict AMI using delta hs-cTnT and its confounders as covariates. The diagnostic performance of model cutoffs was compared to that of the hs-cTnT delta thresholds. Serial logistic regressions were carried out to evaluate the relationship between covariates. RESULTS: The area under the curve of the best-fitted model was 0.95. The model achieved a 90.0% diagnostic accuracy in the validation cohort. The optimal model cutoff yielded comparable performance (90.5% accuracy) to the optimal sex-specific delta thresholds (90.3% accuracy), with 95.8% agreement between the two diagnostic methods. Serial logistic regressions revealed that delta hs-cTnT played a more predominant role in AMI prediction than its confounders, among which sex is more predictive of AMI (total effect coefficient 1.04) than age (total effect coefficient 0.05) and eGFR (total effect coefficient -0.008). CONCLUSIONS: The integrated prediction model incorporating confounding factors does not outperform hs-cTnT delta thresholds. Sex-specific hs-cTnT delta thresholds remain to provide the highest diagnostic accuracy.

Mini-implant assisted posterior intrusion: A quantification of anterior bite closure in nongrowing subjects.

INTRODUCTION: This study aimed to identify the vertical radiographic changes in nongrowing patients after treatment of anterior open bites (AOBs) using mini-implant assisted intrusion and to provide a predictive model to quantify the achievable intrusion. METHODS: This retrospective radiographic study evaluated the dentoskeletal changes in adults using orthodontic mini-implants in 53 treated patients with AOB. Radiographs before and after posterior intrusion were utilized to evaluate the associated changes. Conventional cephalometric analyses provided data for assessment. A paired t test was used to identify significant changes. A regression model (best subsets selection algorithm) was generated to quantify the relationship between mini-implant-assisted intrusion and the resultant change in overbite. A matched, untreated control sample was used for comparison. RESULTS: One hundred percent of AOBs were corrected using mini-implant-assisted intrusion of the maxillary molars. The overbite increased by an average of 3.6 mm. The average amount of maxillary first molar intrusion was 2.67 mm. The mandibular first molar moved an average of 1.93 mm closer to the palatal plane because of an average clockwise mandibular rotation of 0.78°. The occlusal plane steepened by an average of 3.95°. If all other inputs are held constant, 1 mm of intrusion of the maxillary first molar results in a 0.86 mm increase in overbite. CONCLUSIONS: Mini-implant-assisted intrusion successfully treated AOB in adults with significant dentoalveolar but no significant skeletal changes. An average of 2.67 mm of intrusion of the maxillary first molars is achievable with this method showing that 1 mm of intrusion of the maxillary first molar increased overbite by 0.86 mm. Longer periods of intrusion resulted in greater amounts of bite closure.

Grafted and Vanishing Random Subspaces.

The Random Subspace Method (RSM) is an ensemble procedure in which each constituent learner is constructed using a randomly chosen subset of the data features. Regression trees are ideal candidate learners in RSM ensembles. By constructing trees upon different feature subsets, RSM reduces correlation between trees resulting in a stronger ensemble. Furthermore, it lessens computational burden by only considering a subset of the features when building each tree. Despite its apparent advantages, RSM has a notable drawback. In some instances a randomly chosen subspace may lack informative features. This is especially true in situations in which the number of truly informative variables is small relative to the total number of variables. Trees that are constructed using feature subsets lacking informative features can be damaging to the ensemble. Here we present Grafted Random Subspaces (GRS) and Vanishing Random Subspaces (VRS), two novel ensemble procedures designed to remedy the aforementioned drawback by reusing information across trees. Both techniques borrow from RSM by growing individual trees on randomly selected feature subsets. For each tree in a GRS ensemble, the most important variable is identified and guaranteed inclusion into the next q feature subsets. This allows GRS to recycle a promising feature from one tree across several successive trees, effectively grafting the variable into the next q active subsets. In the VRS procedure the least important feature is guaranteed exclusion from the next q feature subsets. This creates a more enriched pool of candidate variables from which the successive feature subsets are drawn.

Prenatal phthalate exposure in relation to placental corticotropin releasing hormone (pCRH) in the CANDLE cohort.

CONTEXT: Phthalates may disrupt maternal-fetal-placental endocrine pathways, affecting pregnancy outcomes and child development. Placental corticotropin releasing hormone (pCRH) is critical for healthy pregnancy and child development, but understudied as a target of endocrine disruption. OBJECTIVE: To examine phthalate metabolite concentrations (as mixtures and individually) in relation to pCRH. DESIGN: Secondary data analysis from a prospective cohort study. SETTING: Prenatal clinics in Tennessee, USA. PATIENTS: 1018 pregnant women (61.4% non-Hispanic Black, 32% non-Hispanic White, 6.6% other) participated in the CANDLE study and provided data. Inclusion criteria included: low-medical-risk singleton pregnancy, age 16-40, and gestational weeks 16-29. INTERVENTION: None. MAIN OUTCOME MEASURES: Plasma pCRH at two visits (mean gestational ages 23.0 and 31.8 weeks) and change in pCRH between visits (ΔpCRH). RESULTS: In weighted quantile sums (WQS) regression models, phthalate mixtures were associated with higher pCRH at Visit 1 (ß = 0.07, 95 %CI: 0.02, 0.11) but lower pCRH at Visit 2 (ß = -0.08, 95 %CI: -0.14, -0.02). In stratified analyses, among women with gestational diabetes (n = 59), phthalate mixtures were associated with lower pCRH at Visit 1 (ß = -0.17, 95 %CI: -0.35, 0.0006) and Visit 2 (ß = -0.35, 95 %CI: -0.50, -0.19), as well as greater ΔpCRH (ß = 0.16, 95 %CI: 0.07, 0.25). Among women with gestational hypertension (n = 102), phthalate mixtures were associated with higher pCRH at Visit 1 (ß = 0.20, 95 %CI: 0.03, 0.36) and Visit 2 (ß = 0.42; 95 %CI: 0.19, 0.64) and lower ΔpCRH (ß = -0.17, 95 %CI: -0.29, -0.06). Significant interactions between individual phthalate metabolites and pregnancy complications were observed. CONCLUSIONS: Phthalates may impact placental CRH secretion, with differing effects across pregnancy. Differences in results between women with and without gestational diabetes and gestational hypertension suggest a need for further research examining whether women with pregnancy complications may be more vulnerable to endocrine-disrupting effects of phthalates.