Timing of antibiotic treatment identifies distinct clinical presentations among patients presenting with suspected septic shock.

Objective: Recent clinical guidelines for sepsis management emphasize immediate antibiotic initiation for suspected septic shock. Though hypotension is a high-risk marker of sepsis severity, prior studies have not considered the precise timing of hypotension in relation to antibiotic initiation and how clinical characteristics and outcomes may differ. Our objective was to evaluate antibiotic initiation in relation to hypotension to characterize differences in sepsis presentation and outcomes in patients with suspected septic shock. Methods: Adults presenting to the emergency department (ED) June 2012-December 2018 diagnosed with sepsis (Sepsis-III electronic health record [EHR] criteria) and hypotension (non-resolving for ≥30 min, systolic blood pressure <90 mmHg) within 24 h. We categorized patients who received antibiotics before hypotension ("early"), 0-60 min after ("immediate"), and >60 min after ("late") treatment. Results: Among 2219 patients, 55% received early treatment, 13% immediate, and 32% late. The late subgroup often presented to the ED with hypotension (median 0 min) but received antibiotics a median of 191 min post-ED presentation. Clinical characteristics notable for this subgroup included higher prevalence of heart failure and liver disease (p < 0.05) and later onset of systemic inflammatory response syndrome (SIRS) criteria compared to early/immediate treatment subgroups (median 87 vs. 35 vs. 20 min, p < 0.0001). After adjustment, there was no difference in clinical outcomes among treatment subgroups. Conclusions: There was significant heterogeneity in presentation and timing of antibiotic initiation for suspected septic shock. Patients with later treatment commonly had hypotension on presentation, had more hypotension-associated comorbidities, and developed overt markers of infection (eg, SIRS) later. While these factors likely contribute to delays in clinician recognition of suspected septic shock, it may not impact sepsis outcomes.

Across race, ethnicity, and language: An intervention to improve advance care planning documentation unmasks health disparities.

BACKGROUND: Racial and ethnic minority groups are less likely to have advance directives and living wills, despite the importance of advanced care planning (ACP) in end-of-life care. We aimed to understand the impact of an intervention to improve ACP documentation across race, ethnicity, and language on hospitalized patients at our institution. METHODS: We launched an intervention to improve the rates of ACP documentation for hospitalized patients aged >75 or with advanced illness defined by the International Classification of Diseases 10th Revision codes. We analyzed ACP completion rates, preintervention, and intervention, and used interrupted time-series analyses to measure the differential impact of the intervention across race, ethnicity, and language. KEY RESULTS: A total of 10,220 patients met the inclusion criteria. Overall rates of ACP documentation improved from 13.9% to 43.7% in the intervention period, with a 2.47% monthly increase in ACP documentation compared to baseline (p < .001). During the intervention period, the rate of ACP documentation increased by 2.72% per month for non-Hispanic White patients (p < .001), by 1.84% per month for Latinx patients (p < .001), and by 1.9% per month for Black patients (p < .001). Differences in the intervention trends between non-Hispanic White and Latinx patients (p = .04) and Black patients (p = .04) were significant. CONCLUSIONS: An intervention designed to improve ACP documentation in hospitalized patients widened a disparity across race and ethnicity with Latinx and Black patients having lower rates of improvement. Our findings reinforce the need to measure the impact of quality improvement interventions on existing health disparities and to implement specific strategies to prevent worsening disparities.

Development and evaluation of a concise nurse-driven non-pharmacological delirium reduction workflow for hospitalized patients: An interrupted time series study.

We created a concise nurse-driven delirium reduction workflow with the aim of reducing delirium rates and length of stay for hospitalized adults. Our nurse-driven workflow included five evidence-based daytime "sunrise" interventions (patient room lights on, blinds up, mobilization/out-of-bed, water within patient's reach and patient awake) and five nighttime "turndown" interventions (patient room lights off, blinds down, television off, noise reduction and pre-set bedtime). Interventions were also chosen because fidelity could be quickly monitored twice daily without patient interruption from outside the room. To evaluate the workflow, we used an interrupted time series study design between 06/01/17 and 05/30/22 to determine if the workflow significantly reduced the unit's delirium rate and average length of stay. Our workflow is feasible to implement and monitor and initially significantly reduced delirium rates but not length of stay. However, the reduction in delirium rates were not sustained following the emergence of the COVID-19 pandemic.

A Focused Screening and Clinical Intervention with Streamlined Outpatient Linkage for Hospitalized Patients with Opioid Use Disorder Experiencing Homelessness.

Background: Patients experiencing homelessness have higher rates of substance use and related mortality, often driven by opioid overdose. Conversely, opioid use disorder (OUD) is a leading risk factor for homelessness. Our goal was to test the efficacy of an electronic health record (EHR) screen in identifying this vulnerable population during hospitalization and to assess the feasibility of a bundled intervention in improving opioid safety. Methods: We assessed patients' housing status, substance use, previous MOUD treatment, barriers to MOUD treatment and readiness to take MOUD in and out of the hospital. For each post discharge follow up call, patients were asked about their MOUD status, barriers accessing treatment, current substance use, and housing status. We also assessed team members perceptions and experiences of the study. Results: We enrolled 32 patients with housing insecurity and OUD. The mean age was 44, the majority self-identified as male (78%), and mostly as White (56%) or Black (38%). At each follow up within the 6-months post-discharge, reach rates were low: 40% of enrollees answered at least 1 call and the highest reach rate (31% of patients) occurred at week 4. At the third and sixth-month follow ups, >50% of subjects still taking MOUD were also using opioids. Conclusion: Our clinician augmented EHR screen accurately identified inpatients experiencing OUD and PEH. This intervention showed high rates of attrition among enrolled patients, even after providing cellphones. The majority of patients who were reached remained adherent to MOUD though they reported significant barriers.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19.

BACKGROUND: Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort. OBJECTIVE: To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients. DESIGNS, SETTINGS, AND PARTICIPANTS: A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021. INTERVENTION: Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation. MAIN OUTCOME AND MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated. RESULTS: We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604). CONCLUSIONS: Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

Social Frailty Index: Development and validation of an index of social attributes predictive of mortality in older adults.

While social characteristics are well-known predictors of mortality, prediction models rely almost exclusively on demographics, medical comorbidities, and function. Lacking an efficient way to summarize the prognostic impact of social factor, many studies exclude social factors altogether. Our objective was to develop and validate a summary measure of social risk and determine its ability to risk-stratify beyond traditional risk models. We examined participants in the Health and Retirement Study, a longitudinal, survey of US older adults. We developed the model from a comprehensive inventory of 183 social characteristics using least absolute shrinkage and selection operator, a penalized regression approach. Then, we assessed the predictive capacity of the model and its ability to improve on traditional prediction models. We studied 8,250 adults aged ≥65 y. Within 4 y of the baseline interview, 22% had died. Drawn from 183 possible predictors, the Social Frailty Index included age, gender, and eight social predictors: neighborhood cleanliness, perceived control over financial situation, meeting with children less than yearly, not working for pay, active with children, volunteering, feeling isolated, and being treated with less courtesy or respect. In the validation cohort, predicted and observed mortality were strongly correlated. Additionally, the Social Frailty Index meaningfully risk-stratified participants beyond the Charlson score (medical comorbidity index) and the Lee Index (comorbidity and function model). The Social Frailty Index includes age, gender, and eight social characteristics and accurately risk-stratifies older adults. The model improves upon commonly used risk prediction tools and has application in clinical, population health, and research settings.

Automated telephone follow-up programs after hospital discharge: Do older adults engage with these programs?

BACKGROUND: Health systems have developed automated telephone call programs to screen and triage patients' post-hospital discharge issues and concerns. The aims of our study were to determine whether and how older adults engage with automated post-hospital discharge telephone programs and to describe the prevalence of patient-reported post-discharge issues. METHODS: We identified all telephone calls made by an urban academic medical center as part of a post-hospital discharge program between May 1, 2018 and April 30, 2019. The program used automated telephone outreach to patients or their caregivers that included 11 distinct steps 3 days post-discharge. All adults discharged home from the hospital, were included, and we categorized patients into ≤64 years, 65-84 years, and ≥85 years age groups. We then compared call reach rate, completeness of 11-step calls and patient-reported issues between age groups. RESULTS: Eighteen thousand and seventy six patients were included. More patients 65-84 years old were reached compared to patients ≤64 years old (84.3% vs. 78.9%, AME 5.52%; 95%CI: 3.58%-7.45%). Completion rates of automated calls for those ≥85 years old were also high. Patients ≥85 years old were more likely to have questions about their follow-up plans and need assistance scheduling appointments compared to those ≤64 years old (19.0% vs. 11.9%, AME 7.0% (95%CI: 2.7%-11.3%). CONCLUSION: Post-hospital automated telephone calls are feasible and effective at reaching older adults. Future work should focus on improving discharge communication to ensure older adults are aware of their follow-up plan and appointments.