Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p

Aerodynamics of a stay cable with helical fillets - Part I:Stability and load characteristics

The aerodynamic behaviour of a bridge stay cable with helical fillets in smooth flow at high Reynolds numbers is presented in this paper. The cable response and related sectional load characteristics were studied experimentally on a 1:1 scale cable section model. The studies showed that a cable with helical fillets inclined 60\ub0 to the flow could experience large amplitude wind induced vibrations and that the occurrence of vibrations were highly dependent on cable surface irregularities. The ambition is not to explain fully the excitation mechanism, but to present global and local influences of the helical fillets on the flow field. It was revealed that the flow field around the cable shifted between semi-stable transition states which took place when the transition from laminar to turbulent flow propagated from the free shear layers to the boundary layer. The transitions would form locally and spread along the cable axis. The helical fillet appeared to dominate the local flow structures when located at an angular position between 40\ub0 and 130\ub0 from the stagnation region. In the stagnation and base regions, the surface irregularities appeared to dominate. Furthermore, the helical fillets displaced the mean stagnation line. The application of quasi-steady theory with the measurement data available appeared not to be able to explain the vibrations.Peer reviewed: YesNRC publication: Ye

Predicting death from surgery for lung cancer: a comparison of two scoring systems in two European countries

Objectives: Current British guidelines advocate the use of risk prediction scores such as Thoracoscore to estimate mortality prior to radical surgery for non-small cell lung cancer (NSCLC). A recent publication used the National Lung Cancer Audit (NLCA) to produce a score to predict 90 day mortality (NLCA score). The aim of this study is to validate the NLCA score, and compare its performance with Thoracoscore. Materials and methods: We performed an internal validation using 2858 surgical patients from NLCA and an external validation using 3191 surgical patients from the Danish Lung Cancer Registry (DLCR). We calculated the proportion that died within 90 days of surgery. The discriminatory power of both scores was assessed by a receiver operating characteristic (ROC) and an area under the curve (AUC) calculation. Results: Ninety day mortality was 5% in both groups. AUC values for internal and external validation of NLCA score and validation of Thoracoscore were 0.68 (95% CI 0.63–0.72), 0.60 (95% CI 0.56–0.65) and 0.60 (95% CI 0.54–0.66) respectively. Post-hoc analysis was performed using NLCA records on 15554 surgical patients to derive summary tables for 30 and 90 day mortality, stratified by procedure type, age and performance status. Conclusions: Neither score performs well enough to be advocated for individual risk stratification prior to lung cancer surgery. It may be that additional physiological parameters are required; however this is a further project. In the interim we propose the use of our summary tables that provide the real-life range of mortality for lobectomy and pneumonectomy

Impact of acute coronary syndrome on clinical outcomes after revascularization with the dual-therapy CD34 antibody-covered sirolimus-eluting Combo stent and the sirolimus-eluting Orsiro stent

OBJECTIVES: To compare the efficacy and safety of the dual-therapy CD34 antibody-covered sirolimus-eluting Combo stent (DTS) and the sirolimus-eluting Orsiro stent (O-SES) in patients with and without acute coronary syndrome (ACS) included in the SORT OUT X study.BACKGROUND: The incidence of target lesion failure (TLF) after treatment with modern drug-eluting stents has been reported to be significantly higher in patients with ACS when compared to patients without ACS. Whether the results from the SORT OUT X study apply to patients with and without ACS remains unknown.METHODS: In total, 3146 patients were randomized to stent implantation with DTS (n = 1578; ACS: n = 856) or O-SES (n = 1568; ACS: n = 854). The primary end point, TLF, was a composite of cardiac death, target-lesion myocardial infarction (MI), or target lesion revascularization (TLR) within 1 year.RESULTS: At 1 year, the rate of TLF was higher in the DTS group compared to the O-SES group, both among patients with ACS (6.7% vs. 4.1%; incidence rate ratio: 1.65 [95% confidence interval, CI: 1.08-2.52]) and without ACS (6.0% vs. 3.2%; incidence rate ratio: 1.88 [95% CI: 1.13-3.14]). The differences were mainly explained by higher rates of TLR, whereas rates of cardiac death and target lesion MI did not differ significantly between the two stent groups in patients with or without ACS CONCLUSION: Compared to the O-SES, the DTS was associated with a higher risk of TLF at 12 months in patients with and without ACS. The differences were mainly explained by higher rates of TLR.</p

Brain Expressed microRNAs Implicated in Schizophrenia Etiology

BACKGROUND: Protein encoding genes have long been the major targets for research in schizophrenia genetics. However, with the identification of regulatory microRNAs (miRNAs) as important in brain development and function, miRNAs genes have emerged as candidates for schizophrenia-associated genetic factors. Indeed, the growing understanding of the regulatory properties and pleiotropic effects that miRNA have on molecular and cellular mechanisms, suggests that alterations in the interactions between miRNAs and their mRNA targets may contribute to phenotypic variation. METHODOLOGY/PRINCIPAL FINDINGS: We have studied the association between schizophrenia and genetic variants of miRNA genes associated with brain-expression using a case-control study design on three Scandinavian samples. Eighteen known SNPs within or near brain-expressed miRNAs in three samples (Danish, Swedish and Norwegian: 420/163/257 schizophrenia patients and 1006/177/293 control subjects), were analyzed. Subsequently, joint analysis of the three samples was performed on SNPs showing marginal association. Two SNPs rs17578796 and rs1700 in hsa-mir-206 (mir-206) and hsa-mit-198 (mir-198) showed nominal significant allelic association to schizophrenia in the Danish and Norwegian sample respectively (P = 0.0021 & p = 0.038), of which only rs17578796 was significant in the joint sample. In-silico analysis revealed that 8 of the 15 genes predicted to be regulated by both mir-206 and mir-198, are transcriptional targets or interaction partners of the JUN, ATF2 and TAF1 connected in a tight network. JUN and two of the miRNA targets (CCND2 and PTPN1) in the network have previously been associated with schizophrenia. CONCLUSIONS/SIGNIFICANCE: We found nominal association between brain-expressed miRNAs and schizophrenia for rs17578796 and rs1700 located in mir-206 and mir-198 respectively. These two miRNAs have a surprising large number (15) of targets in common, eight of which are also connected by the same transcription factors