Prediction Model of Postoperative Severe Hypocalcemia in Patients with Secondary Hyperparathyroidism Based on Logistic Regression and XGBoost Algorithm.

Objective: A predictive model was established based on logistic regression and XGBoost algorithm to investigate the factors related to postoperative hypocalcemia in patients with secondary hyperparathyroidism (SHPT). Methods: A total of 60 SHPT patients who underwent parathyroidectomy (PTX) in our hospital were retrospectively enrolled. All patients were randomly divided into a training set (n = 42) and a test set (n = 18). The clinical data of the patients were analyzed, including gender, age, dialysis time, body mass, and several preoperative biochemical indicators. The multivariate logistic regression and XGBoost algorithm models were used to analyze the independent risk factors for severe postoperative hypocalcemia (SH). The forecasting efficiency of the two prediction models is analyzed. Results: Multivariate logistic regression analysis showed that body mass (OR = 1.203, P = 0.032), age (OR = 1.214, P = 0.035), preoperative PTH (OR = 1.026, P = 0.043), preoperative Ca (OR = 1.062, P = 0.025), and preoperative ALP (OR = 1.031, P = 0.027) were positively correlated with postoperative SH. The top three important features of XGBoost algorithm prediction model were preoperative Ca, preoperative PTH, and preoperative ALP. The area under the curve of the logistic regression and XGBoost algorithm model in the test set was 0.734 (95% CI: 0.595~0.872) and 0.827 (95% CI: 0.722~0.932), respectively. Conclusion: The predictive models based on the logistic regression and XGBoost algorithm model can predict the occurrence of postoperative SH.

A Novel Next-Generation Sequencing-Based Approach for Concurrent Detection of Mitochondrial DNA Copy Number and Mutation.

Numerous studies have identified essential contributions of altered mitochondrial DNA (mtDNA) copy number and mutations in many common disorders, including cancer. To date, capture-based next-generation sequencing (NGS) has been widely applied to detect mtDNA mutations, although it lacks the ability to assess mtDNA copy number. The current strategy for quantifying mtDNA copy number relies mainly on real-time quantitative PCR, which is limited in degraded samples. A novel capture-based NGS approach was developed using both mtDNA and nuclear DNA probes to capture target fragments, enabling simultaneous detection of mtDNA mutations and copy number in different sample types. First, the impact of selecting reference genes on mtDNA copy number calculation was evaluated, and finally, 3 nuclear DNA fragments of 4000 bp were selected as an internal reference for detection. Then, the effective application of this approach was verified in DNA samples of formalin-fixed, paraffin-embedded specimens and body fluids, indicating the widespread applicability. This approach showed more accurate and stable results in detecting mtDNA copy number compared with real-time quantitative PCR in degraded DNA samples. Moreover, data indicated this approach had good reproducibility in detecting both mtDNA copy number and mutations among three sample types. Altogether, a versatile and cost-effective capture-based NGS approach has been developed for concurrent detection of mtDNA copy number and mutations, which has numerous applications in research and diagnosis.