Real-World Implications of Updated Surviving Sepsis Campaign Antibiotic Timing Recommendations.

OBJECTIVE: To evaluate real-world implications of updated Surviving Sepsis Campaign (SSC) recommendations for antibiotic timing. DESIGN: Retrospective cohort study. SETTING: Twelve hospitals in the Southeastern United States between 2017 and 2021. PATIENTS: One hundred sixty-six thousand five hundred fifty-nine adult hospitalized patients treated in the emergency department for suspected serious infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We determined the number and characteristics of patients affected by updated SSC recommendations for initiation of antibiotics that incorporate a risk- and probability-stratified approach. Using an infection prediction model with a cutoff of 0.5 to classify possible vs. probable infection, we found that 30% of the suspected infection cohort would be classified as shock absent, possible infection and thus eligible for the new 3-hour antibiotic recommendation. In real-world practice, this group had a conservative time to antibiotics (median, 5.5 hr; interquartile range [IQR], 3.2-9.8 hr) and low mortality (2%). Patients categorized as shock absent, probable infection had a median time to antibiotics of 3.2 hours (IQR, 2.1-5.1 hr) and mortality of 3%. Patients categorized as shock present, the probable infection had a median time to antibiotics 2.7 hours (IQR, 1.7-4.6 hr) and mortality of 17%, and patients categorized as shock present, the possible infection had a median time to antibiotics 6.9 hours (IQR, 3.5-16.3 hr) and mortality of 12%. CONCLUSIONS: These data support recently updated SSC recommendations to align antibiotic timing targets with risk and probability stratifications. Our results provide empirical support that clinicians and hospitals should not be held to 1-hour targets for patients without shock and with only possible sepsis.

Effect of a Multicomponent Sepsis Transition and Recovery Program on Mortality and Readmissions After Sepsis: The Improving Morbidity During Post-Acute Care Transitions for Sepsis Randomized Clinical Trial.

OBJECTIVES: To evaluate whether a nurse navigator-led, multicomponent Sepsis Transition And Recovery program improves 30-day mortality and readmission outcomes after sepsis hospitalization. DESIG: n: Multisite pragmatic randomized clinical trial. SETTING: Three hospitals in North Carolina from January 2019 to March 2020. PATIENTS: Eligible patients hospitalized for suspected sepsis and deemed high-risk for mortality or readmission by validated internal risk models. INTERVENTIONS: Patients were randomized to receive usual care alone (i.e., routine transition support, outpatient care; n = 342) or additional Sepsis Transition And Recovery support (n = 349). The 30-day intervention involved a multicomponent transition service led by a nurse navigator through telephone and electronic health record communication to facilitate best practice postsepsis care strategies during and after hospitalization including: postdischarge medication review, evaluation for new impairments or symptoms, monitoring comorbidities, and palliative care approach when appropriate. Clinical oversight was provided by a Hospital Medicine Transition Services team. MEASUREMENTS AND MAIN RESULTS: The primary outcome was a composite of mortality or hospital readmission at 30 days. Logistic regression models were constructed to evaluate marginal and conditional odds ratios (adjusted for prognostic covariates: age, comorbidity, and organ dysfunction at enrollment). Among 691 randomized patients (mean age = 63.7 ± 15.1 yr; 52% female), a lower percentage of patients in the Sepsis Transition And Recovery group experienced the primary outcome compared with the usual care group (28.7% vs 33.3%; risk difference, 4.7%; odds ratio, 0.80; 95% CI, 0.58-1.11; adjusted odds ratio, 0.80; 95% CI, 0.64-0.98). There were 74 deaths (Sepsis Transition And Recovery: 33 [9.5%] vs usual care: 41 [12.0%]) and 155 rehospitalizations (Sepsis Transition And Recovery: 71 [20.3%] vs usual care: 84 [24.6%]). CONCLUSIONS: In a multisite randomized clinical trial of patients hospitalized with sepsis, patients provided with a 30-day program using a nurse navigator to provide best practices for postsepsis care experienced a lower proportion of either mortality or rehospitalization within 30 days after discharge. Further research is needed to understand the contextual factors associated with successful implementation.

Insights From Rapid Deployment of a "Virtual Hospital" as Standard Care During the COVID-19 Pandemic.

BACKGROUND: Pandemics disrupt traditional health care operations by overwhelming system resource capacity but also create opportunities for care innovation. OBJECTIVE: To describe the development and rapid deployment of a virtual hospital program, Atrium Health hospital at home (AH-HaH), within a large health care system. DESIGN: Prospective case series. SETTING: Atrium Health, a large integrated health care organization in the southeastern United States. PATIENTS: 1477 patients diagnosed with coronavirus disease 2019 (COVID-19) from 23 March to 7 May 2020 who received care via AH-HaH. INTERVENTION: A virtual hospital model providing proactive home monitoring and hospital-level care through a virtual observation unit (VOU) and a virtual acute care unit (VACU) in the home setting for eligible patients with COVID-19. MEASUREMENTS: Patient demographic characteristics, comorbid conditions, treatments administered (intravenous fluids, antibiotics, supplemental oxygen, and respiratory medications), transfer to inpatient care, and hospital outcomes (length of stay, intensive care unit [ICU] admission, mechanical ventilation, and death) were collected from electronic health record data. RESULTS: 1477 patients received care in either the AH-HaH VOU or VACU or both settings, with a median length of stay of 11 days. Of these, 1293 (88%) patients received care in the VOU only, with 40 (3%) requiring inpatient hospitalization. Of these 40 patients, 16 (40%) spent time in the ICU, 7 (18%) required ventilator support, and 2 (5%) died during their hospital admission. In total, 184 (12%) patients were ever admitted to the VACU, during which 21 patients (11%) required intravenous fluids, 16 (9%) received antibiotics, 40 (22%) required respiratory inhaler or nebulizer treatments, 41 (22%) used supplemental oxygen, and 24 (13%) were admitted as an inpatient to a conventional hospital. Of these 24 patients, 10 (42%) required ICU admission, 1 (3%) required a ventilator, and none died during their hospital admission. LIMITATION: Generalizability is limited to patients with a working telephone and the ability to comply with the monitoring protocols. CONCLUSION: Virtual hospital programs have the potential to provide health systems with additional inpatient capacity during the COVID-19 pandemic and beyond. PRIMARY FUNDING SOURCE: Atrium Health.

First-to-second antibiotic delay and hospital mortality among emergency department patients with suspected sepsis.

OBJECTIVE: To evaluate whether delay between the first and second antibiotic administered for suspected sepsis is associated with hospital mortality. DESIGN: Retrospective cohort. SETTING: Twelve hospitals in Southeastern United States from 2014 to 2017. PATIENTS: 25,717 adults with suspected sepsis presenting to 12 Emergency Departments who received at least two antibiotics within 12 h. MEASUREMENTS AND MAIN RESULTS: The primary exposure was first-to-second antibiotic delay >1 h. We used generalized linear mixed models to model the association between first-to-second antibiotic delay and hospital death in the overall cohort, and in subgroups of patients with and without septic shock. Overall, 13,852 (54%) patients had first-to-second antibiotic delay >1 h and 1666 (7%) died. Adjusting for other risk factors, first-to-second antibiotic delay was associated with increased risk of hospital death in the subgroup of patients with septic shock (OR 1.34; 95% CI: 1.05-1.70), but not among patients without shock (OR 0.99; 95% CI: 0.88-1.12) or in the overall cohort (OR 1.08; 95% CI: 0.97-1.20). CONCLUSIONS: First-to-second antibiotic delay of greater than one hour was associated with an increased risk of hospital death among patients meeting criteria for septic shock but not all patients with suspected sepsis. Tracking and improving first-to-second antibiotic delays may be considered in septic shock.

Protocol for a two-arm pragmatic stepped-wedge hybrid effectiveness-implementation trial evaluating Engagement and Collaborative Management to Proactively Advance Sepsis Survivorship (ENCOMPASS).

BACKGROUND: Sepsis survivors experience high morbidity and mortality, and healthcare systems lack effective strategies to address patient needs after hospital discharge. The Sepsis Transition and Recovery (STAR) program is a navigator-led, telehealth-based multicomponent strategy to provide proactive care coordination and monitoring of high-risk patients using evidence-driven, post-sepsis care tasks. The purpose of this study is to evaluate the effectiveness of STAR to improve outcomes for sepsis patients and to examine contextual factors that influence STAR implementation. METHODS: This study uses a hybrid type I effectiveness-implementation design to concurrently test clinical effectiveness and gather implementation data. The effectiveness evaluation is a two-arm, pragmatic, stepped-wedge cluster randomized controlled trial at eight hospitals in North Carolina comparing clinical outcomes between sepsis survivors who receive Usual Care versus care delivered through STAR. Each hospital begins in a Usual Care control phase and transitions to STAR in a randomly assigned sequence (one every 4 months). During months that a hospital is allocated to Usual Care, all eligible patients will receive usual care. Once a hospital transitions to STAR, all eligible patients will receive STAR during their hospitalization and extending through 90 days from discharge. STAR includes centrally located nurse navigators using telephonic counseling and electronic health record-based support to facilitate best-practice post-sepsis care strategies including post-discharge review of medications, evaluation for new impairments or symptoms, monitoring existing comorbidities, and palliative care referral when appropriate. Adults admitted with suspected sepsis, defined by clinical criteria for infection and organ failure, are included. Planned enrollment is 4032 patients during a 36-month period. The primary effectiveness outcome is the composite of all-cause hospital readmission or mortality within 90 days of discharge. A mixed-methods implementation evaluation will be conducted before, during, and after STAR implementation. DISCUSSION: This pragmatic evaluation will test the effectiveness of STAR to reduce combined hospital readmissions and mortality, while identifying key implementation factors. Results will provide practical information to advance understanding of how to integrate post-sepsis management across care settings and facilitate implementation, dissemination, and sustained utilization of best-practice post-sepsis management strategies in other heterogeneous healthcare delivery systems. TRIAL REGISTRATION: NCT04495946 . Submitted July 7, 2020; Posted August 3, 2020.

Predictive Accuracy of Quick Sequential Organ Failure Assessment for Hospital Mortality Decreases With Increasing Comorbidity Burden Among Patients Admitted for Suspected Infection.

OBJECTIVES: Evaluate the accuracy of the quick Sequential Organ Failure Assessment tool to predict mortality across increasing levels of comorbidity burden. DESIGN: Retrospective observational cohort study. SETTING: Twelve acute care hospitals in the Southeastern United States. PATIENTS: A total of 52,187 patients with suspected infection presenting to the Emergency Department between January 2014 and September 2017. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was hospital mortality. We used electronic health record data to calculate quick Sequential Organ Failure Assessment risk scores from vital signs and laboratory values documented during the first 24 hours. We calculated Charlson Comorbidity Index scores to quantify comorbidity burden. We constructed logistic regression models to evaluate differences in the performance of quick Sequential Organ Failure Assessment greater than or equal to 2 to predict hospital mortality in patients with no documented (Charlson Comorbidity Index = 0), low (Charlson Comorbidity Index = 1-2), moderate (Charlson Comorbidity Index = 3-4), or high (Charlson Comorbidity Index ≥ 5) comorbidity burden. Among the cohort, 2,030 patients died in the hospital (4%). No comorbidities were documented for 5,038 patients (10%), 9,235 patients (18%) had low comorbidity burden, 12,649 patients (24%) had moderate comorbidity burden, and 25,265 patients (48%) had high comorbidity burden. Overall model discrimination for quick Sequential Organ Failure Assessment greater than or equal to 2 was the area under the receiver operating characteristic curve of 0.71 (95% CI, 0.69-0.72). A model including both quick Sequential Organ Failure Assessment and Charlson Comorbidity Index had improved discrimination compared with Charlson Comorbidity Index alone (area under the receiver operating characteristic curve, 0.77; 95% CI, 0.76-0.78 vs area under the curve, 0.61; 95% CI, 0.59-0.62). Discrimination was highest among patients with no documented comorbidities (quick Sequential Organ Failure Assessment area under the receiver operating characteristic curve, 0.84; 95% CI; 0.79-0.89) and lowest among high comorbidity patients (quick Sequential Organ Failure Assessment area under the receiver operating characteristic curve, 0.67; 95% CI, 0.65-0.68). The strength of association between quick Sequential Organ Failure Assessment and mortality ranged from 30.5-fold increased likelihood in patients with no comorbidities to 4.7-fold increased likelihood in patients with high comorbidity. CONCLUSIONS: The accuracy of quick Sequential Organ Failure Assessment to predict hospital mortality diminishes with increasing comorbidity burden. Patients with comorbidities may have baseline abnormalities in quick Sequential Organ Failure Assessment variables that reduce predictive accuracy. Additional research is needed to better understand quick Sequential Organ Failure Assessment performance across different comorbid conditions with modification that incorporates the context of changes to baseline variables.

Patient and care team perspectives on an app to support Hospital at Home admission decision making.

BACKGROUND: Hospital at Home (HaH) programs are used throughout the United States and are beneficial in both providing patients care in environments most comfortable to them and freeing up inpatient beds. Better informing patients about HaH programs, while promoting shared decision-making (SDM), should be prioritized by health systems. SDM apps may promote increased patient agency and understanding of complex HaH care decisions. We previously developed, usability tested, and refined a HaH SDM app. OBJECTIVES: To evaluate the utility of SDM apps in assisting pneumonia patients with HaH admission. METHODS: Usability surveys (N = 16) and semistructured interviews with patients (N = 9) and nurse navigators (N = 3) were utilized to evaluate our app in assisting pneumonia patients as they contemplated HaH admission. Recruitment occurred at three hospitals in the southeastern United States. Surveys were analyzed consistent with their validated measures, while interviews were analyzed using inductive coding methodologies. RESULTS: Patients supported receiving HaH information via an app, with many noting that presenting content via multiple modalities (e.g., videos, pictures, text) was helpful and that the app assisted their care decision. App-guided inquiries into patients' care preferences helped patients visualize their priorities and promoted feelings of agency, while providing important information to care teams. Participants found visuals effective at conveying program details, for example, HaH's in-home setup, which may assist with health literacy challenges. Potential barriers included the need to expand app accessibility for vision impaired and non-English speaking patients. CONCLUSIONS: SDM apps may better inform patients' HaH care decisions, allowing patients self-directed access to information and engagement with visual content, which may address challenges related to health literacy and navigating complex, time-sensitive decisions.

A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.

Evaluating Racial/Ethnic Differences in Care Escalation Among COVID-19 Patients in a Home-Based Hospital.

The novel coronavirus disease 2019 (COVID-19) has infected over 414 million people worldwide with 5.8 million deaths, as of February 2022. Telemedicine-based interventions to expand healthcare systems' capacity and reduce infection risk have rapidly increased during the pandemic, despite concerns regarding equitable access. Atrium Health Hospital at Home (AH-HaH) is a home-based program that provides advanced, hospital-level medical care and monitoring for patients who would otherwise be hospitalized in a traditional setting. Our retrospective cohort study of positive COVID-19 patients who were admitted to AH-HaH aims to investigate whether the rate of care escalation from AH-HaH to traditional hospitalization differed based on patients' racial/ethnic backgrounds. Logistic regression was used to examine the association between care escalation within 14 days from index AH-HaH admission and race/ethnicity. We found approximately one in five patients receiving care for COVID-19 in AH-HaH required care escalation within 14 days. Odds of care escalation were not significantly different for Hispanic or non-Hispanic Blacks compared to non-Hispanic Whites. However, secondary analyses showed that both Hispanic and non-Hispanic Black patients were younger and with fewer comorbidities than non-Hispanic Whites. The study highlights the need for new care models to vigilantly monitor for disparities, so that timely and tailored adaptations can be implemented for vulnerable populations.

Clinical Subtypes of Sepsis Survivors Predict Readmission and Mortality after Hospital Discharge.

Rationale: Sepsis survivors experience adverse outcomes including high rates of postdischarge mortality and rehospitalization. Given the heterogeneity of the condition, using a person-centered framework to identify subtypes within this population with different risks of postdischarge outcomes may optimize postsepsis care. Objectives: To classify individuals into subtypes and assess the association of subtypes with 30-day rehospitalization and mortality. Methods: We conducted a retrospective observational study between January 2014 and October 2017 among 20,745 patients admitted to one of 12 southeastern U.S. hospitals with a clinical definition of sepsis. We used latent class analysis to classify sepsis survivors into subtypes, which were evaluated against 30-day readmission and mortality rates using a specialized regression approach. A secondary analysis evaluated subtypes against readmission rate for ambulatory care-sensitive conditions. Results: Among 20,745 patients, latent class analysis identified five distinct subtypes as the optimal solution. Clinical subtype was associated with 30-day readmission, with the subtype existing poor health with severe illness and complex needs after discharge demonstrating highest risk (35%) and the subtype low risk, barriers to care demonstrating the lowest risk (9%). Forty-seven percent of readmissions in the subtype poor functional status were for ambulatory care-sensitive conditions, whereas 17% of readmissions in the subtype previously healthy with severe illness and complex needs after discharge, barriers to care were for ambulatory care-sensitive conditions. Subtype was significantly associated with 30-day mortality: highest in for existing poor health with severe illness and complex needs after discharge (8%) and lowest for low risk, barriers to care (0.1%). Conclusions: Sepsis survivors can be classified into subtypes representing nuanced constellations of characteristics, with differential 30-day mortality and readmission risk profiles. Predischarge classification may allow an individualized approach to postsepsis care.

Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study.

BACKGROUND: Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS: Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. RESULTS: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 - the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS: Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

The Effect of Outcome Selection on the Performance of Prediction Models in Patients at Risk for Sepsis.

IMPORTANCE: Risk prediction models for patients with suspected sepsis have been derived on and applied to various outcomes, including readily available outcomes such as hospital mortality and ICU admission as well as longer-term mortality outcomes that may be more important to patients. It is unknown how selecting different outcomes influences model performance in patients at risk for sepsis. OBJECTIVES: Evaluate the impact of outcome selection on risk model performance and weighting of individual predictor variables. DESIGN SETTING AND PARTICIPANTS: We retrospectively analyzed adults hospitalized with suspected infection from January 2014 to September 2017 at 12 hospitals. MAIN OUTCOMES AND MEASURES: We used routinely collected clinical data to derive logistic regression models for four outcomes: hospital mortality, composite ICU length of stay greater than 72 hours or hospital mortality, 30-day mortality, and 90-day mortality. We compared the performance of the models using area under the receiver operating characteristic curve and calibration plots. RESULTS: Among 52,184 admissions, 2,030 (4%) experienced hospital mortality, 6,659 (13%) experienced the composite of hospital mortality or ICU length of stay greater than 72 hours, 3,417 (7%) experienced 30-day mortality, and 5,655 (11%) experienced 90-day mortality. Area under the receiver operating characteristic curves decreased when hospital-based models were applied to predict 30-day (hospital mortality = 0.88-0.85; -0.03, composite ICU length of stay greater than 72 hours or hospital mortality = 0.90-0.81; -0.09) and 90-day mortality (hospital mortality = 0.88-0.81; -0.07, composite ICU length of stay greater than 72 hours or hospital mortality = 0.90-0.76; -0.14; all p < 0.01). Models were well calibrated for derived (root-mean-square error = 5-15) but not alternate outcomes (root-mean-square error = 8-35). CONCLUSIONS AND RELEVANCE: Risk models trained to predict readily available hospital-based outcomes in suspected sepsis show poorer discrimination and calibration when applied to 30- and 90-day mortality. Interpretation and application of risk models for patients at risk of sepsis should consider these findings.

Association between Adherence to Recommended Care and Outcomes for Adult Survivors of Sepsis.

Rationale: Postsepsis care recommendations target specific deficits experienced by sepsis survivors in elements such as optimization of medications, screening for functional impairments, monitoring for common and preventable causes of health deterioration, and consideration of palliative care. However, few data are available regarding the application of these elements in clinical practice.Objectives: To quantify the delivery of postsepsis care for patients discharged after hospital admission for sepsis and evaluate the association between receipt of postsepsis care elements and reduced mortality and hospital readmission within 90 days.Methods: We conducted a retrospective chart review of a random sample of patients who were discharged alive after an admission for sepsis (identified from International Classification of Diseases, 10th Revision discharge codes) at 10 hospitals during 2017. We used a structured chart abstraction to determine whether four elements of postsepsis care were provided within 90 days of hospital discharge, per expert recommendations. We used multivariable logistic regression to evaluate the association between receipt of care elements and 90-day hospital readmission and mortality, adjusted for age, comorbidity, length of stay, and discharge disposition.Results: Among 189 sepsis survivors, 117 (62%) had medications optimized, 123 (65%) had screening for functional or mental health impairments, 86 (46%) were monitored for common and preventable causes of health deterioration, and 110 (58%) had care alignment processes documented (i.e., assessed for palliative care or goals of care). Only 20 (11%) received all four care elements within 90 days. Within 90 days of discharge, 66 (35%) patients were readmitted and 33 (17%) died (total patients readmitted or died, n = 82). Receipt of two (odds ratio [OR], 0.26; 95% confidence interval [95% CI], 0.10-0.69) or more (three OR, 0.28; 95% CI, 0.11-0.72; four OR, 0.12; 95% CI, 0.03-0.50) care elements was associated with lower odds of 90-day readmission or 90-day mortality compared with zero or one element documented. Optimization of medications (no medication errors vs. one or more errors; OR, 0.44; 95% CI, 0.21-0.92), documented functional or mental health assessments (physical function plus swallowing/mental health assessments vs. no assessments; OR, 0.14; 95% CI, 0.05-0.40), and documented goals of care or palliative care screening (OR, 0.52; 95% CI, 0.25-1.05; not statistically significant) were associated with lower odds of 90-day readmission or 90-day mortality.Conclusions: In this retrospective cohort study of data from a single health system, we found variable delivery of recommended postsepsis care elements that were associated with reduced morbidity and mortality after hospitalization for sepsis. Implementation strategies to efficiently overcome barriers to adopting recommended postsepsis care may help improve outcomes for sepsis survivors.

Structured, proactive care coordination versus usual care for Improving Morbidity during Post-Acute Care Transitions for Sepsis (IMPACTS): a pragmatic, randomized controlled trial.

BACKGROUND: Hospital mortality for patients with sepsis has recently declined, but sepsis survivors still suffer from significant long-term mortality and morbidity. There are limited data that support effective strategies to address post-discharge management of patients hospitalized with sepsis. METHODS: The Improving Morbidity during Post-Acute Care Transitions for Sepsis (IMPACTS) study is a pragmatic, randomized controlled trial at three hospitals within a single healthcare delivery system comparing clinical outcomes between sepsis survivors who receive usual care versus care delivered through the Sepsis Transition and Recovery (STAR) program. The STAR program includes a centrally located nurse navigator using telephone counseling and electronic health record-based support to facilitate best-practice post-sepsis care strategies for patients during hospitalization and the 30 days after hospital discharge, including post-discharge review of medications, evaluation for new impairments or symptoms, monitoring existing comorbidities, and palliative care referral when appropriate. Adults admitted through the Emergency Department with suspected infection (i.e., antibiotics initiated, bacterial cultures drawn) and deemed, by previously developed risk-stratification models, high risk for readmission or death are included. Eligible patients are randomly allocated 1:1 to either Arm 1, usual care or Arm 2, STAR. Planned enrollment is 708 patients during a 6-month period. The primary outcome is the composite of all-cause hospital readmissions and mortality assessed 30 days post discharge. Secondary outcomes include 30- and 90-day hospital readmissions, mortality, emergency department visits, acute care-free days alive, and acute care and total costs. DISCUSSION: This pragmatic evaluation provides the most comprehensive assessment to date of a strategy to improve delivery of recommended post-sepsis care. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03865602. Registered retrospectively on 6 March 2019.