Using the Stratum-Specific Likelihood Ratio Method to Derive Outcome-Based Hospital Volume Categories for Total Knee Replacement.

BACKGROUND: Evidence of higher hospital volume being associated with improved outcomes for patients undergoing total knee replacement (TKR) is mostly based on arbitrary distribution-based thresholds. OBJECTIVE: We aimed to define outcome-based volume thresholds using data from a national database. METHODS: We used the MedPAR Limited Data Set inpatient data from 2010-2015 to identify patients who had undergone primary TKR. Surgical and TKR specific complications occurring within the index hospitalization and all-cause readmission within 90 days were considered adverse events. We derived an average annual TKR case volume for each hospital and applied the stratum-specific likelihood ratio method to determine volume categories indicative of a similar likelihood of 90-day post-operative complications. Hierarchical multivariable logistic regression with a random intercept for hospital nested within study year and adjusted for patient and hospital characteristics was performed to determine if these volume thresholds were still associated with the odds of 90-day readmission for complications after adjustment. RESULTS: SSLR analysis yielded 4 hospital volume categories based on the likelihood of 90-day postoperative complications: 1-31 (low), 32-127 (medium), 128-248 (high), and 429+ (very high) TKRs performed per year. The results of the hierarchical multivariable logistic regression showed significantly increased odds of 90-day complications at lower volume categories. Sensitivity analyses confirmed our main findings. CONCLUSIONS: This study is the first to provide national-level volume categories that are evidence-based. Publicizing these thresholds may enhance quality measures available to patients, providers, and payors.

The Cost-Effectiveness of Computer-Assisted Compared with Conventional Total Knee Arthroplasty: A Payer's Perspective.

BACKGROUND: Recent evidence showing that computer-assisted total knee arthroplasty (TKA) is associated with better outcomes compared with conventional TKA for patients with end-stage knee osteoarthritis has not been included in economic evaluations of computer-assisted TKA, which are needed to support coverage decisions. This study evaluated the cost-effectiveness of computer-assisted TKA from a payer's perspective, incorporating recent evidence. METHODS: We compared computer-assisted TKA with conventional TKA with regard to costs (in 2022 U.S. dollars) and quality-adjusted life-years (QALYs) using Markov models for elderly patients (≥65 years of age) and patients who were not elderly (55 to 64 years of age). Costs and QALYs were estimated in the lifetime for elderly patients and in the short term for patients who were not elderly, under a bundled payment program and a Fee-for-Service program. Transition probabilities, costs, and QALYs were retrieved from the literature, a national knee arthroplasty registry, and the National Center for Health Statistics. Threshold and probabilistic sensitivity analyses were conducted to examine the robustness of key estimates used in the base-case analysis. Using projected estimates of TKA utilization, the total cost savings of performing computer-assisted TKA rather than conventional TKA were estimated. RESULTS: Compared with conventional TKA, computer-assisted TKA was associated with higher QALYs and lower costs for both elderly patients and patients who were not elderly, regardless of payment programs, making computer-assisted TKA a favorable treatment option. Widespread adoption of computer-assisted TKA in all U.S. patients would result in an estimated total cost saving of $1 billion for payers. CONCLUSIONS: Compared with conventional TKA, computer-assisted TKA reduces costs to payers while providing favorable outcomes. Payers may consider providing additional payment incentives to providers for performing computer-assisted TKA, to achieve outcome improvement and cost control by facilitating widespread adoption of computer-assisted TKA. LEVEL OF EVIDENCE: Economic and Decision Analysis Level II. See Instructions for Authors for a complete description of levels of evidence.

Step cadence as a novel objective postoperative recovery metric in children who undergo laparoscopic appendectomy.

BACKGROUND: Daily step counts from consumer wearable devices have been used to objectively assess postsurgical recovery in children. However, step cadence, defined as steps taken per minute, may be a more specific measure of physiologic status. The purpose of this study is to define objective normative physical activity recovery trajectories after laparoscopic appendectomy using this novel metric. We hypothesized that patients would have a progressive increase in peak cadence until reaching a plateau representing baseline status, and this would occur earlier for simple compared with complicated appendicitis. METHODS: Children aged 3 to 18 years were enrolled after laparoscopic appendectomy for simple or complicated appendicitis between March 2019 and December 2022 at a tertiary children's hospital. Participants wore a Fitbit for 21 postoperative days. The peak 1-minute cadence and peak 30-minute cadence were determined each postoperative day. Piecewise linear regression was conducted to generate normative peak step cadence recovery trajectories for simple and complicated appendicitis. RESULTS: A total of 147 children met criteria (53.7% complicated appendicitis). Patients with simple appendicitis reached plateau postoperative day 10 at a mean peak 1-minute cadence of 111 steps/minute and a mean peak 30-minute cadence of 77 steps/minute. The complicated appendicitis recovery trajectory reached a plateau postoperative day 13 at a mean peak 1-minute cadence of 106 steps/minute and postoperative day 15 at a mean peak 30-minute cadence of 75 steps/minute. CONCLUSION: Using step cadence, we defined procedure-specific normative peak cadence recovery trajectories after laparoscopic appendectomy. This can empower clinicians to set data-driven expectations for recovery after surgery and establish the groundwork for consumer wearable devices as a post-discharge remote monitoring tool.