Causes and timing of 30-day rehospitalization from skilled nursing facilities after a hospital admission for pneumonia or sepsis

Discharge to Skilled Nursing Facility Is a Risk Factor for Readmission: A Nationwide Propensity-Matched Study

INTRODUCTION

General surgery procedures place stress on geriatric patients, and postdischarge care options should be evaluated. We compared the association of discharge to a skilled nursing facility (SNF) versus home on patient readmission.

METHODS

We retrospectively reviewed the Nationwide Readmission Database (2016-2019) and included patients ≥65 y who underwent a general surgery procedure between January and September. Our primary outcome was 30-d readmissions. Our secondary outcome was predictors of readmission after discharge to an SNF. We performed a 1:1 propensity-matched analysis adjusting for patient demographics and hospital course to compare patients discharged to an SNF with patients discharged home. We performed a sensitivity analysis on patients undergoing emergency procedures and a stepwise regression to identify predictors of readmission.

RESULTS

Among 140,056 included patients, 33,916 (24.2%) were discharged to an SNF. In the matched population of 19,763 pairs, 30-d readmission was higher in patients discharged to an SNF. The most common diagnosis at readmission was sepsis, and a greater proportion of patients discharged to an SNF were readmitted for sepsis. In the sensitivity analysis, emergency surgery patients discharged to an SNF had higher 30-d readmission. Higher illness severity during the index admission and living in a small or fringe county of a large metropolitan area were among the predictors of readmission in patients discharged to an SNF, while high household income was protective.

CONCLUSIONS

Discharge to an SNF compared to patients discharged home was associated with a higher readmission. Future studies need to identify the patient and facility factors responsible for this disparity.

'Sit down and thrash it out': opportunities for expanding ethics consultation during conflict resolution in long-term care

OBJECTIVE

To identify the frequency and nature of care conflict dilemmas that United States long-term care providers encounter, response strategies, and use of ethics resources to assist with dispute resolution.

DESIGN

An online cross-sectional survey was distributed to the Society for Post-Acute and Long-Term Care Medicine (AMDA).

RESULTS

Two-thirds of participants, primarily medical directors, have rejected surrogate instructions and 71% have managed family conflict. Conflict over treatment decisions and issues interpreting advance directives were frequently reported. Half of facilities lack a formal dispute mediation policy. Only five respondents have called an ethics consult for assistance.

CONCLUSION

Ethically tense care conflicts commonly arise in long-term and post-acute care facilities. Few facility procedures incorporate ethics resources into actual practice. Recommendations are made to create actionable policy, increase access to ethics services, and support staff skill development in order to improve the end-of-life care experiences for patients, families, and care facility staff.

Increasing illness severity of skilled nursing facility patients over time: Implications for readmission penalties

BACKGROUND

Current financial penalties for rehospitalization of skilled nursing facilities (SNFs) patients are based in part on the studies by Ouslander et al., 2011, and Mor et al., 2010, demonstrating that many SNF hospitalizations were avoidable. With increasing age, complex illness severity, and use of SNFs for subacute rehabilitation, readmission metrics and financial penalties based on previous data may be due for reevaluation.

METHODS

Retrospective electronic medical record (EMR) review of 21,591 admissions and discharges between 2010 and 2019 inclusive. Data extracted included demographics, LACE, Charlson comorbidity index (CCI), and simplified HOSPITAL score parameters. The scores were calculated for the study years from the extracted data. Patients readmitted to the hospital within 30 days were identified.

RESULTS

Mean yearly score of all three indices rose steadily: LACE score 10.76-12.04 (0.43 estimated annual increase, 95% CI [0.39, 0.46]), CCI 4.26-5.05 (0.31 estimated annual increase, 95% CI [0.27, 0.34]), and simplified HOSPITAL score 3.46-4.03 (0.21 estimated annual increase, 95% CI [0.18, 0.24]). The estimated probability of readmission across observed CCI scores ranged from 15.4% to 15.9%, 95% CI bounds (10.8%, 22.7%). The estimated probability of readmission across observed LACE scores ranged from 4.7% to 36.3%, 95% CI bounds (3.4%, 54.7%). The estimated probability of readmission across observed HOSPITAL scores ranged from 5.8% to 54.1%, 95% CI bounds (6.2%, 66.0%).

CONCLUSIONS AND IMPLICATIONS

The study confirms anecdotal experience that the illness acuity of patients admitted to SNFs increased progressively over time and was associated with an increased risk of 30-day readmissions to the hospital. Our study suggests that the use of clinically validated readmission risk assessment tools instead of the Skilled Nursing Facility Value-Based Purchasing Program (SNF VBP) current risk adjustors may be a more accurate reflection of the current illness severity of a facility's patient population at the time of payment adjustment.

Accelerating Detection and Intervention for Sepsis in Skilled Nursing Facilities Using a Sepsis Pathway

BACKGROUND

Early detection of sepsis decreases mortality in hospitals, but recognition of sepsis is often delayed in skilled nursing facilities (SNFs).

LOCAL PROBLEM

A local SNF in the northeastern United States sought to use a standardized sepsis pathway to prevent hospital readmissions due to sepsis.

METHODS

A pre-/postimplementation design was used for this project. Outcome measures included sepsis detection and treatment, length of stay in the SNF, sepsis-related hospital transfer rate, mortality rate, and predictors of clinical outcomes.

INTERVENTIONS

A SNF sepsis pathway was developed based on current sepsis detection tools. The pathway incorporated a sepsis screening tool and a sepsis bundle. Implementation of the pathway involved education of nurses and certified nursing assistants on the pathway.

RESULTS

A total of 178 patients were included in data analysis (81 preimplementation and 97 implementation). Sepsis recognition increased from 56% to 86% ( P < .001), and sepsis-related hospital transfers decreased from 68% to 44% ( P = .07). Laboratory testing for lactate, white blood cell count, and blood cultures increased, and sepsis intervention rates significantly improved ( P < .001).

CONCLUSIONS

Implementing a modified SNF sepsis pathway accelerated identification of sepsis and improved clinical outcomes.

Passing the SNF Test: A Secondary Analysis of a Sepsis Transition Intervention Trial Among Patients Discharged to Post-Acute Care

OBJECTIVES

Sepsis survivors discharged to post-acute care facilities experience high rates of mortality and hospital readmission. This study compared the effects of a Sepsis Transition and Recovery (STAR) program vs usual care (UC) on 30-day mortality and hospital readmission among sepsis survivors discharged to post-acute care.

DESIGN

Secondary analysis of a multisite pragmatic randomized clinical trial.

SETTING AND PARTICIPANTS

Sepsis survivors discharged to post-acute care.

METHODS

We conducted a secondary analysis of patients from the IMPACTS (Improving Morbidity During Post-Acute Care Transitions for Sepsis) randomized clinical trial who were discharged to post-acute care. IMPACTS evaluated the effectiveness of STAR, a nurse-navigator-led program to deliver best practice post-sepsis care. Subjects were randomized to receive either STAR or UC. The primary outcome was 30-day readmission and mortality. We also evaluated hospital-free days alive as a secondary outcome.

RESULTS

Of 691 patients enrolled in IMPACTS, 175 (25%) were discharged to post-acute care [143 (82%) to skilled nursing facilities, 12 (7%) to long-term acute care hospitals, and 20 (11%) to inpatient rehabilitation]. Of these, 87 received UC and 88 received the STAR intervention. The composite 30-day all-cause mortality and readmission endpoint occurred in 26 (29.9%) patients in the UC group vs 18 (20.5%) in the STAR group [risk difference -9.4% (95% CI -22.2 to 3.4); adjusted odds ratio 0.58 (95% CI 0.28 to 1.17)]. Separately, 30-day all-cause mortality was 8.1% in the UC group compared with 5.7% in the STAR group [risk difference -2.4% (95% CI -9.9 to 5.1)] and 30-day all-cause readmission was 26.4% in the UC group compared with 17.1% in the STAR program [risk difference -9.4% (95% CI -21.5 to 2.8)].

CONCLUSIONS AND IMPLICATIONS

There are few proven interventions to reduce readmission among patients discharged to post-acute care facilities. These results suggest the STAR program may reduce 30-day mortality and readmission rates among sepsis survivors discharged to post-acute care facilities.

Clinical Impact of a Sepsis Alert System Plus Electronic Sepsis Navigator Using the Epic Sepsis Prediction Model in the Emergency Department

BACKGROUND

The Epic Sepsis Prediction Model (SPM) is a proprietary sepsis prediction algorithm that calculates a score correlating with the likelihood of an International Classification of Diseases, Ninth Revision code for sepsis.

OBJECTIVE

This study aimed to assess the clinical impact of an electronic sepsis alert and navigator using the Epic SPM on time to initial antimicrobial delivery.

METHODS

We performed a retrospective review of a nonrandomized intervention of an electronic sepsis alert system and navigator using the Epic SPM. Data from the SPM site (site A) was compared with contemporaneous data from hospitals within the same health care system (sites B-D) and historical data from site A. Nonintervention sites used a systemic inflammatory response syndrome (SIRS)-based alert without a sepsis navigator.

RESULTS

A total of 5368 admissions met inclusion criteria. Time to initial antimicrobial delivery from emergency department arrival was 3.33 h (interquartile range [IQR] 2.10-5.37 h) at site A, 3.22 h (IQR 1.97-5.60; p = 0.437, reference site A) at sites B-D, and 6.20 h (IQR 3.49-11.61 h; p < 0.001, reference site A) at site A historical. After adjustment using matching weights, there was no difference in time from threshold SPM score to initial antimicrobial between contemporaneous sites. Adjusted time to initial antimicrobial improved by 2.87 h (p < 0.001) at site A compared with site A historical.

CONCLUSIONS

Implementation of an electronic sepsis alert system plus navigator using the Epic SPM showed no difference in time to initial antimicrobial delivery between the contemporaneous SPM alert plus sepsis navigator site and the SIRS-based electronic alert sites within the same health care system.

Integration of patient experience factors improves readmission prediction

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.