Effect of Smoking on Short-term Postoperative Complications After Elective Upper Extremity Surgery.

Background: There is a paucity of literature exploring the impact of smoking on short-term complications, readmissions, and reoperations after elective upper extremity surgery using a large multicenter national database. We hypothesized that smokers will have an increased rate of complications, readmissions, and reoperations compared with a cohort of nonsmokers undergoing elective upper extremity surgery. Methods: Patient data were collected from the American College of Surgeons National Surgical Quality Improvement Program database between the years 2012 and 2017. Patients were included if they underwent elective surgery of the upper extremity using 338 predetermined Current Procedural Terminology codes. The data collected were divided into patient demographics, comorbidities, perioperative variables, and 30-day complications. Current smoking status was defined as smoking within 1 year prior to surgery. The incidence of surgical complications, reoperations, and readmissions was compared between the 2 cohorts using multivariable regression analysis. Results: Of the 107 943 patients undergoing elective surgeries of the upper extremity, 73 806 met the inclusion criteria. Of these, 57 986 (78.6%) were nonsmokers in the year prior to surgery, and 15 820 (21.4%) were current smokers. Between these groups, current smokers were younger (P < .001), more often men (P < .001), had lower body mass index (P < .001), and more often underwent procedures that involved bone manipulation (P < .001). Multivariate regression analysis defined current smoking as significantly associated with overall surgical site complications, superficial surgical site infections, deep surgical site infections, reoperation, and readmission. Conclusion: Current smoking was significantly associated with an increase in all surgical site complications, readmissions, and reoperations after elective upper extremity surgery. Surgeons should consider smoking a modifiable risk factor for postoperative complications and appropriately counsel patients on outcomes and complications given the elective nature of upper extremity surgery.

fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control.

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. METHODOLOGY: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated 'ChIP' sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/.

GAPWM: a genetic algorithm method for optimizing a position weight matrix.

MOTIVATION: Position weight matrices (PMWs) are simple models commonly used in motif-finding algorithms to identify short functional elements, such as cis-regulatory motifs, on genes. When few experimentally verified motifs are available, estimation of the PWM may be poor. The resultant PWM may not reliably discriminate a true motif from a false one. While experimentally identifying such motifs remains time-consuming and expensive, low-resolution binding data from techniques such as ChIP-on-chip and ChIP-PET have become available. We propose a novel but simple method to improve a poorly estimated PWM using ChIP data. METHODOLOGY: Starting from an existing PWM, a set of ChIP sequences, and a set of background sequences, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area under the receiver operating characteristic (ROC) curve. GAPWM can easily incorporate prior information such as base conservation. We tested our method on two PMWs (Oct4/Sox2 and p53) using three recently published ChIP data sets (human Oct4, mouse Oct4 and human p53). RESULTS: GAPWM substantially increased the sensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM. Furthermore, it still functioned when the starting PWM contained a major error. The ROC performance of GAPWM compared favorably with that of MEME and others. With increasing availability of ChIP data, our method provides an alternative for obtaining high-quality PWMs for genome-wide identification of transcription factor binding sites. AVAILABILITY: The C source code and all data used in this report are available at http://dir.niehs.nih.gov/dirbb/gapwm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.