BACKGROUND With an aging population, elderly patients with multiple comorbidities are more frequently undergoing spine surgery and may be at increased risk for complications. Objective measurement of frailty may predict the incidence of postoperative adverse events. OBJECTIVE To investigate the associations between preoperative frailty and postoperative spine surgery outcomes including mortality, length of stay, readmission, surgical site infection, and venous thromboembolic disease. METHODS As part of a system-wide quality improvement initiative, frailty assessment was added to the routine assessment of patients considering spine surgery beginning in July 2016. Frailty was assessed with the Risk Analysis Index (RAI), and patients were categorized as nonfrail (RAI 0-29) or prefrail/frail (RAI ≥ 30). Comparisons between nonfrail and prefrail/frail patients were analyzed using Fisher's exact test for categorical data or by Wilcoxon rank sum tests for continuous data. RESULTS From August 2016 through September 2018, 668 patients (age of 59.5 ± 13.3 yr) had a preoperative RAI score recorded and underwent scheduled spine surgery. Prefrail and frail patients suffered comparatively higher rates of mortality at 90 d (1.9% vs 0.2%, P < .05) and 1 yr (5.1% vs 1.2%, P < .01) from the procedure date. They also had longer in-hospital length of stay (LOS) (3.9 d ± 3.6 vs 3.1 d ± 2.8, P < .001) and higher rates of 60 d (14.6% vs 8.2%, P < .05) and 90 d (15.8% vs 9.8%, P < .05) readmissions. CONCLUSION Preoperative frailty, as measured by the RAI, was associated with an increased risk of readmission and 90-d and 1-yr mortality following spine surgery. The RAI can be used to stratify spine patients and inform preoperative surgical decision making.

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Background Processes for transferring patients to higher acuity facilities lack a standardized approach to prognostication, increasing the risk for low value care that imposes significant burdens on patients and their families with unclear benefits. We sought to develop a rapid and feasible tool for predicting mortality using variables readily available at the time of hospital transfer. Methods and findings All work was carried out at a single, large, multi-hospital integrated healthcare system. We used a retrospective cohort for model development consisting of patients aged 18 years or older transferred into the healthcare system from another hospital, hospice, skilled nursing or other healthcare facility with an admission priority of direct emergency admit. The cohort was randomly divided into training and test sets to develop first a 54-variable, and then a 14-variable gradient boosting model to predict the primary outcome of all cause in-hospital mortality. Secondary outcomes included 30-day and 90-day mortality and transition to comfort measures only or hospice care. For model validation, we used a prospective cohort consisting of all patients transferred to a single, tertiary care hospital from one of the 3 referring hospitals, excluding patients transferred for myocardial infarction or maternal labor and delivery. Prospective validation was performed by using a web-based tool to calculate the risk of mortality at the time of transfer. Observed outcomes were compared to predicted outcomes to assess model performance. The development cohort included 20,985 patients with 1,937 (9.2%) in-hospital mortalities, 2,884 (13.7%) 30-day mortalities, and 3,899 (18.6%) 90-day mortalities. The 14-variable gradient boosting model effectively predicted in-hospital, 30-day and 90-day mortality (c = 0.903 [95% CI:0.891–0.916]), c = 0.877 [95% CI:0.864–0.890]), and c = 0.869 [95% CI:0.857–0.881], respectively). The tool was proven feasible and valid for bedside implementation in a prospective cohort of 679 sequentially transferred patients for whom the bedside nurse calculated a SafeNET score at the time of transfer, taking only 4–5 minutes per patient with discrimination consistent with the development sample for in-hospital, 30-day and 90-day mortality (c = 0.836 [95%CI: 0.751–0.921], 0.815 [95% CI: 0.730–0.900], and 0.794 [95% CI: 0.725–0.864], respectively). Conclusions The SafeNET algorithm is feasible and valid for real-time, bedside mortality risk prediction at the time of hospital transfer. Work is ongoing to build pathways triggered by this score that direct needed resources to the patients at greatest risk of poor outcomes.

OBJECTIVES To compare rates of 30‐ and 90‐day hospital readmissions and observation or emergency department (ED) returns of older adults using the University of Pittsburgh Medical Center (UPMC) Health Plan Home Transitions (HT) with those of Medicare fee‐for‐service (FFS) controls without HT. DESIGN Retrospective cohort study. SETTING Analysis of home health and hospital records from 8 UPMC hospitals in Allegheny County, Pennsylvania, from July 1, 2015, to April 30, 2017. PARTICIPANTS HT program participants (n=1,900) and controls (n=1,300). INTERVENTION HT is a care transitions program aimed at preventing readmission that identifies older adults at risk of readmission using a robust inclusion algorithm; deploys a multidisciplinary care team, including a nurse practitioner (NP), a social worker (SW), or both; and provides a multimodal service including personalized care planning, education, treatment, monitoring, and communication facilitation. MEASUREMENT We used multivariable logistic regression to determine the effects of HT on the odds of hospital readmission and observation or ED return, controlling for index admission participant characteristics and home health process measures. RESULTS The adjusted odds of 30‐day readmission was 0.31 (95% confidence interval (CI) = 0.11–0.87, P = .03) and of 90‐day readmission was 0.47 (95% CI=CI = 0.26–0.85, P = .01), for participants at medium risk of readmission in HT who received a team visit. The adjusted odds of 30‐day readmission was 0.29 (95% CI = 0.10–0.83, P = .02) for participants at high risk of readmission in HT who received a team visit. The adjusted odds of 30‐day observation or ED return was 1.90 (95% CI = 1.28–2.82, P = .001) for participants at medium risk of readmission in HT who received a team visit. CONCLUSION The HT program may be associated with lower odds of 30‐ and 90‐day hospital readmission and counterbalancing higher odds of observation or ED return. J Am Geriatr Soc 67:156–163, 2019.

BACKGROUND: Inpatient addiction medicine consultation services (AMCS) have grown rapidly, but there is limited research of their impact on patient outcomes. OBJECTIVE: To examine whether AMCS is associated with all-cause mortality and hospital utilization postdischarge. DESIGN: This was a propensity-score-matchedcasecontrol study from 2018 to 2020. PARTICIPANTS: The intervention group included patients referred to the AMCS from October 2018 to March 2020. Matched control participants included patients hospitalized from October 2017 to September 2018 at an urban academic hospital with a large suburban and rural catchment area. MAIN MEASURES: The effect of treatment was estimated as the difference between the proportion of subjects experiencing the event (7-day and 30-day readmission, emergency department visits, and mortality within 90 days) for each group in the matched sample. KEY RESULTS: There were 711 patients in the intervention group and 2172 patients in the control group. The most common substance use disorders among the intervention group were primary alcohol use disorder (n=181; 25.5%) and primary opioid use disorder (n=175, 24.6%) with over a third with polysubstance use (n=257, 36.1%). Intervention patients showed a reduction in 90-day mortality post-hospital discharge (average treatment effect [ATE]: −2.35%, 95% CI: −3.57, −1.13; p-value <0.001) compared to propensity-matched controls. We found a statistically significant reduction in 7-day hospital readmission by 2.15% (95% CI: −3.65, −0.65; p=0.005) and a nonsignificant reduction in 30-day readmission (ATE: −2.38%, 95% CI: −5.20, 0.45; p=0.099). There was a statistically significant increase in 30-day emergency department visits (ATE: 5.32%, 95% CI: 2.19, 8.46; 0.001) compared to matched controls. CONCLUSIONS: There was a reduction in 90-day allcause mortality for the AMCS intervention group compared to matched controls, although the impact on hospital utilization was mixed. AMCS are systems interventions that are effective tools to improve patient health and reduce all-cause mortality.