Faculty Bibliography

UNLABELLED:Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES/OBJECTIVE:To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN SETTING AND PARTICIPANTS/METHODS:This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016-May 2017) and after (June 2017-November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES/METHODS:The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS:< 0.001). CONCLUSIONS AND RELEVANCE/CONCLUSIONS:Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.

OBJECTIVES:The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator calculates risk of postoperative complications utilizing clinically apparent preoperative variables. If validated for patients with gynecologic cancers, this can be an effective tool in to use for shared decision-making, especially in the older (70+ years of age) patient population for whom surgical risks and potential loss of independence is increased. The primary objective of this study was to evaluate the ability of the ACS NSQIP surgical risk calculator to predict discharge to a post-acute care among older (age 70+ years) gynecologic oncology patients undergoing laparotomy. The secondary objectives were to assess its ability to predict postoperative complications and death. METHODS:This was a retrospective cohort study of gynecologic oncology patients 70+ years of age undergoing laparotomy. Surgical procedures, 21 preoperative variables, postoperative complications, and patient disposition were abstracted from the medical record. Risk scores for seven postoperative complications and discharge to post-acute care were calculated. The association between risk scores and outcomes were assessed using logistic regression and predictive ability was evaluated using the c-statistic and Brier score. RESULTS:204 surgeries were performed on 200 patients between January 1, 2009 and December 31, 2013. The mean age was 76.3 ± 5.1 years; 87% were independent at baseline. A total of 79 (41%) were discharged to post-acute care. The calculator's ability to predict discharge to post-acute care was reasonable (c- statistic =0.708, Brier = 0.205). Although the calculator did not accurately predict all postoperative complications, the calculator's ability to predict death was strong (c-statistic = 0.811, Brier = 0.015). CONCLUSION:For older patients with an elevated calculated risk of discharge to post acute care the possibility of discharge to post-acute care should be discussed preoperatively. For patients with a higher risk of death, non-surgical management options should be considered when available.