BACKGROUND The role of enhanced recovery after surgery (ERAS) pathways implementation has not been previously explored in adult deformity patients. OBJECTIVE To determine the impact of ERAS pathways implementation in adult patients undergoing open thoraco-lumbar-pelvic fusion for degenerative scoliosis on postoperative outcome, opioid consumption, and unplanned readmission rates. METHODS In this retrospective single-center study, we included 124 consecutive patients who underwent open thoraco-lumbar-pelvic fusion from October 2016 to February 2019 for degenerative scoliosis. Primary outcomes consisted of postoperative supplementary opioid consumption in morphine equivalent dose (MED), postoperative complications, and readmission rates within the postoperative 90-d window. RESULTS There were 67 patients in the ERAS group, and 57 patients served as pre-ERAS controls. Average patient age was 69 yr. The groups had comparable demographic and intraoperative variables. ERAS patients had a significantly lower rate of postoperative supplemental opioid consumption (248.05 vs 314.05 MED, P = .04), a lower rate of urinary retention requiring catheterization (5.97% vs 19.3%, P = .024) and of severe constipation (1.49% vs 31.57%, P < .0001), and fewer readmissions after their surgery (2.98% vs 28.07%, P = .0001). CONCLUSION A comprehensive multidisciplinary approach to complex spine surgery can reduce opioid intake, postoperative urinary retention and severe constipation, and unplanned 90-d readmissions in the elderly adult population.

Predicting the genetic variance (VG) in a biparental population has been difficult. Our objective was to determine whether the population mean, VG, and mean of the top 10% of progeny in a cross can be predicted effectively from genomewide marker effects estimated from a diverse panel of inbreds. Eight maize (Zea mays L.) crosses that differed in their predicted mean and VG were evaluated for plant and ear height and growing degree days to silking. Each cross was represented by 120 to 144 random F3 lines that were evaluated in balanced experiments at three locations in Minnesota in 2017. Correlations between the observed and predicted means of each breeding population were significant (r ≥ 0.80, P = 0.05) for all three traits. However, correlations between the observed and predicted VG were nonsignificant (−0.24 to 0.14) for the three traits. Correlations between the observed and predicted mean of the top 10% of progeny in each cross were significant (P = 0.05) for plant height (0.72), but not for ear height and silking date. These results for predicting the mean of the top 10% of progeny reflected the ability to predict the mean but not VG. We concluded that the mean, but not the VG, of biparental crosses can be effectively predicted from genomewide marker effects estimated from a diverse panel of inbreds.

Genomic selection combines phenotypic and molecular marker data from a training population to predict the genotypic values of untested lines. It can improve breeding efficiency as large pools of untested lines can be evaluated for selection. Training population (TP) composition is one of the most important factors affecting the accuracy of genomic prediction. The University of Minnesota wheat breeding program implements genomic selection at the F5 stage for Fusarium head blight (FHB) resistance. This study used field data for FHB resistance in wheat (Triticum aestivum L.) to investigate the use of small-size TPs designed with and without stratified sampling for three FHB traits in three different F5 populations (TP17, TP18, and TP19). We also compared the accuracies of these two TP design methods with the accuracy obtained from a large size TP. Lastly, we evaluated the impact on trait predictions when the parents of F5 lines were included in the TP. We found that the small size TP selected randomly, without stratification, had the lowest predictive ability across the three F5 populations and across the three traits. This trend was statistically significant (p = 0.05) for all three traits in TP17 and two traits in TP18. Designing a small-size TP by stratified sampling led to a higher accuracy than a large-size TP in most traits across TP18 and TP19; this is because stratified sampling allowed the selection of a small set of closely related lines. We also observed that the addition of parental lines to the TP and evaluating the TP in two replications led to an increase in predictive abilities in most cases.

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