Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background: Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures. Methods: 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors. Results: ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001). Conclusions: ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost

Employing machine learning to predict adverse acute post-surgical outcomes following elective ulnar collateral ligament reconstruction

Background: Ulnar collateral ligament reconstruction ameliorates valgus elbow instability in various patient populations, including overhead athletes, patients with acute UCL rupture following high energy trauma, and those with chronic, subclinical elbow laxity. This study aims to explore machine learning algorithms to identify risk factors in patients undergoing elective UCL reconstruction in the ambulatory setting to predict postoperative outcomes. Methods: RStudio was used to create a filtering code to identify adult patients who underwent elective UCL reconstruction from 2008 to 2018 in the American college of surgeons national surgical quality improvement program database. Patients were analyzed using six ML algorithms, which were trained to predict outcomes such as extended length of stay, non-home discharge, and adverse events based on various patient characteristics and surgical variables. Algorithmic performance was then assessed and top performing algorithms underwent further analysis to determine relative feature importance using a permutation feature importance method. Results: ML exhibited excellent performance in predicting LOS, with an average AUC of 0.953, similar to that of logistic regression. Regarding NHD, ML demonstrated a 60.8% increase in AUC compared to LR. In predicting AAE, ML achieved an average AUC that was 12.7% higher than LR. Conclusions: The highly predictive capability of ML indicates the possibility to represent a procedure-specific complementary tool for the preoperative risk stratification process. This study provides a comprehensive analysis of UCL reconstruction in the management and outcomes of any patient, regardless of age or activity level

Comparison of Intact <em>Arabidopsis thaliana</em> Leaf Transcript Profiles during Treatment with Inhibitors of Mitochondrial Electron Transport and TCA Cycle

<div><p>Plant mitochondria signal to the nucleus leading to altered transcription of nuclear genes by a process called mitochondrial retrograde regulation (MRR). MRR is implicated in metabolic homeostasis and responses to stress conditions. Mitochondrial reactive oxygen species (mtROS) are a MRR signaling component, but whether all MRR requires ROS is not established. Inhibition of the cytochrome respiratory pathway by antimycin A (AA) or the TCA cycle by monofluoroacetate (MFA), each of which initiates MRR, can increase ROS production in some plant cells. We found that for AA and MFA applied to leaves of soil-grown <em>Arabidopsis thaliana</em> plants, ROS production increased with AA, but not with MFA, allowing comparison of transcript profiles under different ROS conditions during MRR. Variation in transcript accumulation over time for eight nuclear encoded mitochondrial protein genes suggested operation of both common and distinct signaling pathways between the two treatments. Consequences of mitochondrial perturbations for the whole transcriptome were examined by microarray analyses. Expression of 1316 and 606 genes was altered by AA and MFA, respectively. A subset of genes was similarly affected by both treatments, including genes encoding photosynthesis-related proteins. MFA treatment resulted in more down-regulation. Functional gene category (MapMan) and cluster analyses showed that genes with expression levels affected by perturbation from AA or MFA inhibition were most similarly affected by biotic stresses such as pathogens. Overall, the data provide further evidence for the presence of mtROS-independent MRR signaling, and support the proposed involvement of MRR and mitochondrial function in plant responses to biotic stress.</p> </div

Changes in transcript levels for nucleus-encoded mitochondrial protein genes that were altered in expression by AA, MFA, or both treatments.

<p>Genes listed either show significant change for one or both treatments or, for some of the genes discussed in the text, are listed regardless of significance. Genes that encode proteins for which there is proteomic data indicating mitochondrial localization or association but not previously annotated as such based on prediction algorithms are indicated by an asterisk <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#pone.0044339-Heazlewood1" target="_blank">[43]</a> and/or a carrot (J.-P.Yu, unpublished). Otherwise, genes were determined to encode mitochondrial proteins based on annotations for the arrays (see ‘<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#s4" target="_blank">Materials and Methods</a>’) or The Arabidopsis Information Resource database. Number symbols indicate the nine genes that were significantly induced by both inhibitor treatments. Fold-change (FC) is the ratio between transcript levels in inhibitor treated plants compared to control treated plants. Ala, alanine; cyt, cytochrome; DH, dehydrogenase; mito., mitochondrial; prot., protein; put., putative; sub., subunit; UQ, ubiquinone.</p

Relationship between AA, MFA, and other stress treatments based on cluster analyses.

<p>From public data bases, 46 experiments were chosen that used treatments of leaves or seedlings, and Affymetrix ATH1 arrays. The expression patterns of nuclear genes that were statistically significantly (q≤0.05) altered in expression by AA (a) or that were significantly altered in expression by MFA (b) were compared to their expression patterns in the transcriptomes resulting from the 46 stress treatments (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#pone.0044339.s005" target="_blank">Resource S5</a>) and from the other inhibitor treatment. Their expression ratios in treatment versus control were compared with those from AA or MFA treatment using Cluster (Hierarchical Clustering/Average Linkage Clustering). The resulting array clusters were visualized using TreeView. The query gene set (i.e., transcriptome) for each inhibitor is indicated by a box in a and b, while the non-query inhibitor gene set is circled. A photomorphogenesis experiment transcript subset that served as an outgroup is circled in a and b. Pathogen and pathogen-related treatments clustering near AA and MFA are delimited by a green box; pathogen treatments elsewhere in the tree are designated with arrow heads to the right. Correlation coefficients for the tree nodes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#pone.0044339.s006" target="_blank">Resource S6</a>) range, left to right, in a. from −0.112 to 0.898 and in b. from −0.105 to 0.897. Numbers of designated nodes and their correlation coefficients are shown in the figures.</p

Venn diagram comparing numbers of genes whose expression was affected by 20 µM AA and/or by 5 mM MFA.

<p>The total number of genes with q≤0.05 that were up-regulated (a) or down-regulated (b) are shown; In parentheses is shown the number of these genes up-regulated (a) or down-regulated (b) 2-fold or more by each treatment. Note that expression of 9 genes (not shown in the diagram; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#pone.0044339.s001" target="_blank">Resource S1</a>) changed in opposite directions in response to the two inhibitions with all 9 induced by AA but repressed by MFA.</p

Correlation of gene expression changes from cytochrome pathway inhibition (AA) and TCA cycle inhibition (MFA).

<p>Graph of fold change (log₂) from AA treatment (abscissa) versus fold change (log₂) from MFA treatment (ordinate) for 215 genes that showed transcript level changes with q≤0.05 for both treatments.</p

DCF fluorescence used to measure ROS production over time in leaves of intact plants treated with 5 mM MFA (triangles) or 20 µM AA (squares) or control treated (diamonds).

<p>At each time point after inhibitor application, leaves were harvested and incubated with H₂DCFDA. DCF fluorescence was measured in aliquots from the incubation medium as described in ‘<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044339#s4" target="_blank">Materials and Methods</a>.’ The graphed points are averages of three separate bioreplicate experiments. In each experiment, measurements were made for each of three replicates from plants in independent pots. Error bars show the standard error among the three experiments.</p