OBJECTIVES: The aims of this study were to compare the performance of machine learning methods for the prediction of the medical costs associated with spinal fusion in terms of profit or loss in Taiwan Diagnosis-Related Groups (Tw-DRGs) and to apply these methods to explore the important factors associated with the medical costs of spinal fusion. METHODS: A data set was obtained from a regional hospital in Taoyuan city in Taiwan, which contained data from 2010 to 2013 on patients of Tw-DRG49702 (posterior and other spinal fusion without complications or comorbidities). Naïve-Bayesian, support vector machines, logistic regression, C4.5 decision tree, and random forest methods were employed for prediction using WEKA 3.8.1. RESULTS: Five hundred thirty-two cases were categorized as belonging to the Tw-DRG49702 group. The mean medical cost was US $4,549.7, and the mean age of the patients was 62.4 years. The mean length of stay was 9.3 days. The length of stay was an important variable in terms of determining medical costs for patients undergoing spinal fusion. The random forest method had the best predictive performance in comparison to the other methods, achieving an accuracy of 84.30%, a sensitivity of 71.4%, a specificity of 92.2%, and an AUC of 0.904. CONCLUSIONS: Our study demonstrated that the random forest model can be employed to predict the medical costs of Tw-DRG49702, and could inform hospital strategy in terms of increasing the financial management efficiency of this operation.
Area Under Curve ; Costs and Cost Analysis ; Dataset ; Decision Trees ; Diagnosis-Related Groups ; Financial Management ; Forests ; Humans ; Length of Stay ; Logistic Models ; Machine Learning ; Methods ; Sensitivity and Specificity ; Spinal Fusion ; Support Vector Machine ; Taiwan

AIMTo complete comprehensive haplotype analysis of USP26 for both fertile and infertile men.

METHODSTwo hundred infertile men with severe oligospermia or non-obstructive azoospermia were subjected to sequence analysis for the entire coding sequences of the USP26 gene. Two hundred men with proven fertility were genotyped by primer extension methods. Allele/genotype frequencies, linkage disequilibrium (LD) characteristics and haplotypes of fertile men were compared with infertile men.

RESULTSThe allele frequencies of five single nucleotide polymorphisms (370-371insACA, 494T>C, 576G>A, ss6202791C>T, 1737G>A) were significantly higher in infertile patients than control subjects. The major haplotypes in infertile men were TACCGA (28% of the population), TGCCGA (15%), TACCAA (8%), TGCCAA (6%), TATCAA (5%) and CATCAA (5%). The major haplotypes for the control subjects were TACCGA (58% of the population), CACCGA (7%), CATCGA (6%) and TGCCGA (5%). Haplotypes TGCCGA, TATCAA, CATCAA, CATCGC, TACCAA and TGCCAA were over-transmitted in patients with spermatogenic defect, whereas haplotypes TACCGA, CACCGA, and CATCGA were under-transmitted in these patients.

CONCLUSIONSome USP26 alleles and haplotypes are associated with spermatogenic defect in the Han nationality in Taiwan, China.

Adult ; Alleles ; Azoospermia ; epidemiology ; genetics ; Cysteine Endopeptidases ; genetics ; DNA Primers ; Gene Frequency ; Genetic Variation ; Genotype ; Haplotypes ; Humans ; Infertility, Male ; epidemiology ; genetics ; Linkage Disequilibrium ; Male ; Multigene Family ; Oligospermia ; epidemiology ; genetics ; Polymorphism, Genetic ; Spermatogenesis ; genetics ; physiology ; Taiwan ; epidemiology

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.