Hospital Factors Are More Important than Volume in Predicting Outcome and Failure to Rescue in Medicare Beneficiaries Undergoing Robotic Lobectomy
Presented During:
Monday, May 8, 2023: 3:18PM - 3:21PM
Los Angeles Convention Center
Posted Room Name:
Exhibit Hall
Abstract No:
P0141
Submission Type:
Abstract Submission
Authors:
J. W. Hayanga (1), Jason Lamb (1), Stuart Campbell (1), J. Hunter Mehaffey (1), Ghulam Abbas (1), Vinay Badhwar (1), Alper Toker (1)
Institutions:
(1) West Virginia University, Morgantown, WV
Submitting Author:
*J. W. Awori Hayanga
-
Contact Me
West Virginia University
West Virginia University
Co-Author(s):
Jason Lamb
-
Contact Me
West Virginia University
West Virginia University
Stuart Campbell
-
Contact Me
West Virginia University
West Virginia University
J. Hunter Mehaffey
-
Contact Me
West Virginia University
West Virginia University
Ghulam Abbas
-
Contact Me
West Virginia University
West Virginia University
*Vinay Badhwar
-
Contact Me
West Virginia University
West Virginia University
*Seyfi Toker
-
Contact Me
West Virginia University
West Virginia University
Presenting Author:
*J. W. Awori Hayanga
-
Contact Me
West Virginia University
West Virginia University
Abstract:
OBJECTIVE
Surgical volume is known to influence failure to rescue (FTR), defined as death following a complication. Robotic lung surgery continues to expand despite outcome variability between hospitals. We sought to estimate the contribution of hospital-based factors on outcomes and FTR following robotic lobectomy.
METHODS
Using the Centers for Medicare and Medicaid Services inpatient claims database, we evaluated all patients aged 65 and older with a diagnosis of lung cancer undergoing robotic upper lobectomy between January 2018 and December 2020. We excluded patients who had a segmentectomy, sublobar, wedge and bronchoplastic resections, those with metastatic or non-malignant disease, and those with a history of neoadjuvant chemotherapy. Primary outcomes included FTR rate, length of stay (LOS), readmissions, conversion to open, complications, and costs. We analyzed hospitals by tertiles of volume (Low <9, Medium 9-20; High >20). We utilized the Medicare Mortality index (MMI), a marker of overall hospital performance, and analyzed hospitals by tertile (low <0.04; medium 0.04-0.13; high >0.13). Propensity score models were adjusted for confounding using goodness-of-fit.
RESULTS
Data pertaining to 4,317 patients who underwent robotic resection were analyzed. After propensity score balancing, patients from tertiles of lowest volume and highest MMI had higher costs ($34,222 vs. $30,316, p = 0.006) p < 0.001), as well as higher mortality (OR 7.46; 95% CI 2.67-28.2, p < 0.001) and death within 60 days (OR 17.1; 95% CI 5.43-87.4). Compared to high volume hospitals, low volume centers had the highest incidence of conversion to open, atelectasis, respiratory failure, hemorrhagic anemia, death, length of stay (LOS), and costs (each p<0.001). The C-statistic for volume as a predictor of FTR was 0.6. Hospitals in the highest tertile of MMI had highest incidence of conversion to open surgery (p=0.01), pneumothorax (p=0.02), atelectasis (p<0.001), and respiratory failure (p<0.001). They also had highest mortality, readmissions, LOS, costs (each p<0.001), and shortest survival (p<0.001). The C-statistic for MMI was 0.8. (Figure)
CONCLUSION
The MMI incorporates hospital-based factors in the adjudication of outcomes and may be a more sensitive predictor of FTR rates than volume alone. Combining MMI and volume may provide a metric that can guide quality improvement and cost effectiveness measures in hospitals seeking to implement robotic lung
Surgical volume is known to influence failure to rescue (FTR), defined as death following a complication. Robotic lung surgery continues to expand despite outcome variability between hospitals. We sought to estimate the contribution of hospital-based factors on outcomes and FTR following robotic lobectomy.
METHODS
Using the Centers for Medicare and Medicaid Services inpatient claims database, we evaluated all patients aged 65 and older with a diagnosis of lung cancer undergoing robotic upper lobectomy between January 2018 and December 2020. We excluded patients who had a segmentectomy, sublobar, wedge and bronchoplastic resections, those with metastatic or non-malignant disease, and those with a history of neoadjuvant chemotherapy. Primary outcomes included FTR rate, length of stay (LOS), readmissions, conversion to open, complications, and costs. We analyzed hospitals by tertiles of volume (Low <9, Medium 9-20; High >20). We utilized the Medicare Mortality index (MMI), a marker of overall hospital performance, and analyzed hospitals by tertile (low <0.04; medium 0.04-0.13; high >0.13). Propensity score models were adjusted for confounding using goodness-of-fit.
RESULTS
Data pertaining to 4,317 patients who underwent robotic resection were analyzed. After propensity score balancing, patients from tertiles of lowest volume and highest MMI had higher costs ($34,222 vs. $30,316, p = 0.006) p < 0.001), as well as higher mortality (OR 7.46; 95% CI 2.67-28.2, p < 0.001) and death within 60 days (OR 17.1; 95% CI 5.43-87.4). Compared to high volume hospitals, low volume centers had the highest incidence of conversion to open, atelectasis, respiratory failure, hemorrhagic anemia, death, length of stay (LOS), and costs (each p<0.001). The C-statistic for volume as a predictor of FTR was 0.6. Hospitals in the highest tertile of MMI had highest incidence of conversion to open surgery (p=0.01), pneumothorax (p=0.02), atelectasis (p<0.001), and respiratory failure (p<0.001). They also had highest mortality, readmissions, LOS, costs (each p<0.001), and shortest survival (p<0.001). The C-statistic for MMI was 0.8. (Figure)
CONCLUSION
The MMI incorporates hospital-based factors in the adjudication of outcomes and may be a more sensitive predictor of FTR rates than volume alone. Combining MMI and volume may provide a metric that can guide quality improvement and cost effectiveness measures in hospitals seeking to implement robotic lung
Categories:
Lung Cancer
Image or Table
