1348. Indirect Standardization to Improve Comparison of Children’s Hospitals’ Antimicrobial Use

Background Antimicrobial use (AU) measured by days of therapy per 1000 patient-days (DOT/1000pd), the most established metric, varies widely between children’s hospitals despite robust adoption of antimicrobial stewardship. Differences in diagnoses and procedures (case mix) between hospitals are a source of AU variation not included in adjustment methods such as the Standardized Antimicrobial Administration Ratio. In this study, we evaluated an indirect standardization method to adjust children’s hospital AU for case mix. Methods This multicenter retrospective cohort study included 51 children’s hospitals participating in the Pediatric Health Information System database from 2016-2018. All inpatient, observation, and neonatal admissions were included, with a total of 2,558,948 discharges. Hospitalizations were grouped into 83 strata defined based on All Patients Refined Diagnosis Related Groups (APR-DRGs). Observed to expected (O:E) ratios were calculated by indirect standardization of mean antibiotic DOT per case, with expected values from 2016-2018 and observed values from 2018, and compared to DOT/1000pd. Outlier hospitals were defined by O:E z-scores corresponding to below 10th percentile (low outlier) and above 90th percentile (high outlier). Results Antibacterial DOT/1000pd ranged from 345 to 776 (2.2-fold variation from lowest to highest), whereas O:E ratios ranged from 0.8 to 1.14 (1.4-fold variation from lowest to highest) (Figure 1). O:E ratios were moderately correlated with DOT/1000pd (correlation estimate 0.45; 95% CI 0.19-0.64; p=0.0008). Three high outlier hospitals and 6 low outlier hospitals were identified. Examining hospitals with comparably high DOT/1000pd but discordant O:E ratios, differences could be explained by variation in both case mix and condition-specific AU within strata defined by APR-DRGs. Figure 1. Individual hospitals labeled on the X-axis, ordered by level of antibacterial DOT/1000pd (left axis), represented by bars. Diamonds represent O:E ratios derived by indirect standardization (right axis). Outlier hospitals (low and high) are highlighted in yellow. Dashed horizontal lines represent 10th percentile (lower) and 90th percentile (upper) limits of the O:E ratio distribution. Conclusion The observed variation in DOT/1000pd between hospitals is reduced when indirect standardization is applied to account for case mix differences. This approach can be adapted for more specific uses including clinical conditions, patient populations, or antimicrobial agents. Indirect standardization may enhance stewardship efforts by providing adjusted comparisons that incorporate case mix differences between hospitals. Disclosures All Authors: No reported disclosures

Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers under High Outlier Ratios

Efficient detection of outliers from massive data with a high outlier ratio is challenging but not explicitly discussed yet. In such a case, existing methods either suffer from poor robustness or require expensive computations. This paper proposes a Low-rank based Efficient Outlier Detection (LEOD) framework to achieve favorable robustness against high outlier ratios with much cheaper computations. Specifically, it is worth highlighting the following aspects of LEOD: (1) Our framework exploits the low-rank structure embedded in the similarity matrix and considers inliers/outliers equally based on this low-rank structure, which facilitates us to encourage satisfying robustness with low computational cost later; (2) A novel re-weighting algorithm is derived as a new general solution to the constrained eigenvalue problem, which is a major bottleneck for the optimization process. Instead of the high space and time complexity (O((2n)2)/O((2n)3)) required by the classic solution, our algorithm enjoys O(n) space complexity and a faster optimization speed in the experiments; (3) A new alternative formulation is proposed for further acceleration of the solution process, where a cheap closed-form solution can be obtained. Experiments show that LEOD achieves strong robustness under an outlier ratio from 20% to 60%, while it is at most 100 times more memory efficient and 1000 times faster than its previous counterpart that attains comparable performance. The codes of LEOD are publicly available at https://github.com/demonzyj56/LEOD.

GLOBAL ROTATION ESTIMATION USING WEIGHTED ITERATIVE LIE ALGEBRAIC AVERAGING

In this paper we present an approach for a weighted rotation averaging to estimate absolute rotations from relative rotations between two images for a set of multiple overlapping images. The solution does not depend on initial values for the unknown parameters and is robust against outliers. Our approach is one part of a solution for a global image orientation. Often relative rotations are not free from outliers, thus we use the redundancy in available pairwise relative rotations and present a novel graph-based algorithm to detect and eliminate inconsistent rotations. The remaining relative rotations are input to a weighted least squares adjustment performed in the Lie algebra of the rotation manifold <i>SO</i>(3) to obtain absolute orientation parameters for each image. Weights are determined using the prior information we derived from the estimation of the relative rotations. Because we use the Lie algebra of <i>SO</i>(3) for averaging no subsequent adaptation of the results has to be performed but the lossless projection to the manifold. We evaluate our approach on synthetic and real data. Our approach often is able to detect and eliminate all outliers from the relative rotations even if very high outlier rates are present. We show that we improve the quality of the estimated absolute rotations by introducing individual weights for the relative rotations based on various indicators. In comparison with the state-of-the-art in recent publications to global image orientation we achieve best results in the examined datasets.

Risk-Adjusted Pathologic Margin Positivity Rate As a Quality Indicator in Rectal Cancer Surgery

Purpose Margin positivity after rectal cancer resection is associated with poorer outcomes. We previously developed an instrument for calculating hospital risk-adjusted margin positivity rate (RAMP) that allows identification of performance-based outliers and may represent a rectal cancer surgery quality metric. Methods This was an observational cohort study of patients with rectal cancer within the National Cancer Data Base (2003 to 2005). Hospital performance was categorized as low outlier (better than expected), high outlier (worse than expected), or non-RAMP outlier using standard observed-to-expected methodology. The association between outlier status and overall risk of death at 5 years was evaluated using Cox shared frailty modeling. Results Among 32,354 patients with cancer (mean age, 63.8 ± 13.2 years; 56.7% male; 87.3% white) treated at 1,349 hospitals (4.9% high outlier, 0.7% low outlier), 5.6% of patients were treated at high outliers and 3.0% were treated at low outliers. Various structural (academic status and volume), process (pathologic nodal evaluation and neoadjuvant radiation therapy use), and outcome (sphincter preservation, readmission, and 30-day postoperative mortality) measures were significantly associated with outlier status. Five-year overall survival was better at low outliers (79.9%) compared with high outliers (64.9%) and nonoutliers (68.9%; log-rank test, P < .001). Risk of death was lower at low outliers compared with high outliers (hazard ratio [HR], 0.61; 95% CI, 0.50 to 0.75) and nonoutliers (HR, 0.69; 95% CI, 0.57 to 0.83). Risk of death was higher at high outliers compared with nonoutliers (HR, 1.12; 95% CI, 1.03 to 1.23). Conclusion Hospital RAMP outlier status is a rectal cancer surgery composite metric that reliably captures hospital quality across all levels of care and could be integrated into existing quality improvement initiatives for hospital performance.