Acute Care at Home During the COVID-19 Pandemic Surge in Boston

Safety of Same-Day Discharge after Bariatric Surgery: Retrospective 6-Year North American Analysis

BACKGROUND

With increasing implementation of Enhanced Recovery After Surgery protocols and rising demand for inpatient hospital beds accentuated by COVID-19, there has been interest in same-day discharge (SDD) for bariatric surgery. The aim of this study was to determine the national trends, safety profile, and risk factors for complications of SDD for minimally invasive bariatric surgery.

STUDY DESIGN

We analyzed the MBSAQIP database from 2016 to 2021 to characterize trends in SDD for minimally invasive bariatric operation. Multivariate logistic regression was performed on preoperative patient characteristics predictive of increased complications associated with SDD. A comparative analysis of postoperative outcomes within 30 days was performed for SDD and admission after 1:1 nearest neighbor propensity score matching for patient demographics and preoperative comorbidities.

RESULTS

SDD increased from 2.4% in 2016 to 7.4% in 2021. Major preoperative factors associated with increased complications for SDD included Black race, history of MI, renal insufficiency, deep vein thrombosis, and smoking. SDD for Roux-en-Y gastric bypass had 72% increased risk of postoperative complications compared with sleeve gastrectomy. The overall major complications were lower in SDD cohort vs admission cohort (odds ratio [OR] 0.62, p < 0.01). However, there was a significant increase in deaths within 30 days (OR 2.11, p = 0.01), cardiac arrest (OR 2.73; p < 0.01), and dehydration requiring treatment (OR 1.33; p < 0.01) in SDD cohort compared with admission cohort.

CONCLUSIONS

Nationally, there has been a rise in SDD for bariatric operation from 2016 to 2021. Matched analysis demonstrates that SDD is associated with a significantly higher mortality rate. Additionally, the risk of complications with SDD is higher for RYGB compared with sleeve gastrectomy. Therefore, further studies are required to appropriately select patients for whom bariatric surgery can be safely performed as an outpatient.

Impact of COVID-19 Pandemic Surge on Surgical Outcomes: A Retrospective Study

BACKGROUND

The COVID-19 pandemic posed significant challenges to healthcare systems worldwide, including surgical care. While many studies examined the effect of the pandemic on different patient outcomes, there are none to date examining the impact of the pandemic surge on surgical outcomes. Our aim is to evaluate the impact of the COVID-19 surges on surgical outcomes using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database.

METHODS

A single-center retrospective analysis of 7436 patients who underwent surgery between February 2020 and December 2022 was conducted. Patients were divided into those who underwent surgery during the surge of the pandemic (n = 1217) or outside that period (n = 6219). Primary outcomes were 30-day mortality and morbidity. Secondary outcomes included 30-day mortality, operation time, transfusion, reoperation, and specific postoperative complications. Multivariable logistic regression was used in our analysis. All analyses were conducted using the software "R" version 4.2.1. Statistical significance was set at α = .05 level.

RESULTS

After adjusting for confounders, we found no significant difference in 30-day mortality and morbidity (OR: 1.06, 95% CI: .89-1.226, P = .5173) or 30-day mortality only (OR: 1.39, 95% CI: .788-2.14, P = .1364) between the two groups. No significant differences were observed in secondary outcomes. Sensitivity analyses yielded similar results to the multivariable logistic regression.

CONCLUSION

We found no evidence of increased 30-day mortality and morbidity in patients undergoing surgery during the COVID-19 surges compared to those undergoing surgery outside that period. Our results suggest that surgical care was maintained despite the challenges of the pandemic surges.

The Hospitalist Movement 25 Years Later

Hospitalists are generalists who specialize in the care of hospitalized patients. In the 25 years since the term hospitalist was coined, the field of hospital medicine has grown exponentially and established a substantial footprint in the medical community. There are now more hospitalists than practicing physicians in any other internal medicine subspecialty. Several key forces catalyzed the growth in the field of hospital medicine, including the quality, safety, and value movements; residency duty hour restrictions; the emergence of electronic health records; and the COVID-19 pandemic. Looking ahead, we see new opportunities in the realms of technology and telemedicine, and challenges persist in regard to balancing financial considerations with increasing workload and burnout. Hospitalists must remain nimble and seize emerging opportunities to continue supporting the field's prominence and growth.

Hospital-Level Care at Home for Acutely Ill Adults in Rural Settings: Proof of Concept

Residents in rural areas face barriers to accessing acute care. Rural home hospital (RHH) or delivery of acute care at home could represent an important clinical care model. This study assessed the feasibility and acceptability of RHH as a substitute to traditional hospital care. Patients were cared for by a remote RHH attending physician and an RHH registered nurse deployed to the home. The study team conducted daily check-ins with RHH clinicians to assess workflows for completion. Surveys assessed patient experience and qualitative interviews assessed perceived acceptability, safety, and quality of care. We completed qualitative analysis of the interviews and coded qualitative data into domains and subdomains through an iterative process. RHH was successfully deployed to three acutely ill patients in rural Utah. RHH admission, daily care, and discharge processes were accomplished for each patient. From qualitative analysis, we identified four domains: (1) Perceived comfort level during RHH admission, (2) Perceived safety during RHH admission, (3) Perceived quality of care during RHH admission, and (4) Perception of RHH workflows. We found acute care was delivered to rural homes with satisfactory patient and clinician experience. Team dynamics, technology build, robust clinical and operational workflows, and care coordination were important to a successful admission. Learnings from this study can inform program design and training for RHH teams and startup for larger RHH evaluation. Home hospital care is expanding rapidly in the United States and RHH could represent an important clinical care model.

Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data

BACKGROUND

Computer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection algorithms is lacking, particularly in external real-world data sets. This study's objective was to evaluate a hybrid machine learning model's ability to classify eight arrhythmias from a single-lead ECG signal from acutely ill patients.

METHODS

This cross-sectional external retrospective evaluation of a previously trained hybrid machine learning model against an ECG reading team in the setting of home hospital care (acute care delivered at home substituting for traditional hospital care) draws from patients admitted at two hospitals in Boston, Massachusetts, USA between 12 June 2017 and 23 November 2019. We calculated classifier statistics for each arrhythmia, all arrhythmias and strips where the model identified normal sinus rhythm.

RESULTS

The model analysed 2 680 162 min of single-lead ECG data from 423 patients and identified 691 478 arrhythmias. Patients had a mean age of 70 years (SD, 18), 60% were female and 45% were white. For any arrhythmia, the model had a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, a negative predictive value of 93% and an F1 Score of 99%. Performance was best for pause (F1 Score, 99%) and worst for paroxysmal supraventricular tachycardia (F1 Score, 92%). The model's false positive rate for any arrhythmia was 0.2%, ranging from 0.4% for pause to 7.2% for paroxysmal supraventricular tachycardia. The false negative rate for any arrhythmia was 1.9%.

CONCLUSIONS

A hybrid machine learning model was effective at classifying common cardiac arrhythmias from a single-lead ECG in real-world data.

Staff Successes and Challenges with Telecommunications-Facilitated Patient Care in Hybrid Hospital-at-Home during the COVID-19 Pandemic

Technology-enhanced hospital-at-home (H@H), commonly referred to as hybrid H@H, became more widely adopted during the COVID-19 pandemic. We conducted focus group interviews with Mayo Clinic staff members (n = 14) delivering hybrid H@H in three separate locations-a rural community health system (Northwest Wisconsin), the nation's largest city by area (Jacksonville, FL), and a desert metropolitan area (Scottsdale, AZ)-to understand staff experiences with implementing a new care delivery model and using new technology to monitor patients at home during the pandemic. Using a grounded theory lens, transcripts were analyzed to identify themes. Staff reported that hybrid H@H is a complex care coordination and communication initiative, that hybrid H@H faces site-specific challenges modulated by population density and state policies, and that many patients are receiving uniquely high-quality care through hybrid H@H, partly enabled by advances in technology. Participant responses amplify the need for additional qualitative research with hybrid H@H staff to identify areas for improvement in the deployment of new models of care enabled by modern technology.

Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset

The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO₂). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO₂, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms' median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden.

Remote vs In-home Physician Visits for Hospital-Level Care at Home: A Randomized Clinical Trial

IMPORTANCE

Home hospital care is the substitutive provision of home-based acute care services usually associated with a traditional inpatient hospital. Many home hospital models require a physician to see patients at home daily, which may hinder scalability. Whether remote physician visits can safely substitute for most in-home visits is unknown.

OBJECTIVE

To compare remote and in-home physician care.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial assessed 172 adult patients at an academic medical center and community hospital who required hospital-level care for select acute conditions, including infection, heart failure, chronic obstructive pulmonary disease, and asthma, between August 3, 2019, and March 26, 2020; follow-up ended April 26, 2020.

INTERVENTIONS

All patients received acute care at home, including in-home nurse or paramedic visits, intravenous medications, remote monitoring, and point-of-care testing. Patients were randomized to receive physician care remotely (initial in-home visit followed by daily video visit facilitated by the home hospital nurse) vs in-home care (daily in-home physician visit). In the remote care group, the physician could choose to see the patient at home beyond the first visit if it was felt to be medically necessary.

MAIN OUTCOMES AND MEASURES

The primary outcome was the number of adverse events, compared using multivariable Poisson regression at a noninferiority threshold of 10 events per 100 patients. Adverse events included a fall, pressure injury, and delirium. Secondary outcomes included the Picker Patient Experience Questionnaire 15 score (scale of 0-15, with 0 indicating worst patient experience and 15 indicating best patient experience) and 30-day readmission rates.

RESULTS

A total of 172 patients (84 receiving remote care and 88 receiving in-home physician care [control group]) were randomized; enrollment was terminated early because of COVID-19. The mean (SD) age was 69.3 (18.0) years, 97 patients (56.4%) were female, 77 (45.0%) were White, and 42 (24.4%) lived alone. Mean adjusted adverse event count was 6.8 per 100 patients for remote care patients vs 3.9 per 100 patients for control patients, for a difference of 2.8 (95% CI, -3.3 to 8.9), supporting noninferiority. For remote care vs control patients, the mean adjusted Picker Patient Experience Questionnaire 15 score difference was -0.22 (95% CI, -1.00 to 0.56), supporting noninferiority. The mean adjusted 30-day readmission absolute rate difference was 2.28% (95% CI, -3.23% to 7.79%), which was inconclusive. Of patients in the remote group, 16 (19.0%) required in-home visits beyond the first visit.

CONCLUSIONS AND RELEVANCE

In this study, remote physician visits were noninferior to in-home physician visits during home hospital care for adverse events and patient experience, although in-home physician care was necessary to support many patients receiving remote care. Our findings may allow for a more efficient, scalable home hospital approach but require further research.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04080570.

A research agenda for hospital at home

BACKGROUND

Hospital at home (HaH) provides hospital-level care at home as a substitute for traditional hospital care. Interest in HaH is increasing markedly. While multiple studies of HaH have demonstrated that HaH provides safe, high-quality, cost-effective care, there remain many unanswered research questions. The objective of this study is to develop a research agenda to guide future HaH-related research.

METHODS

Survey of attendees of first World HaH Congress 2019 for input on research for the future HaH development. Selection and ranking of important topic areas for future HaH-related research. Development of research domains and research questions and issues using grounded theory approach, supplemented by focused literature reviews.

RESULTS

240 conference attendees responded to the survey (response rate, 55.3%). The majority were from Europe (64%) and North America (11%) and were HaH program leaders (29%), HaH physicians (27%), and researchers (13%). Nine research domains for future HaH research were identified: 1) definition of the HaH model of care; 2) the HaH clinical model; 3) measurement and outcomes of HaH; 4) patient and caregiver experience with HaH; 5) education and training of HaH clinicians; 6) technology and telehealth for HaH; 7) regulatory and payment issues in HaH; 8) implementation and scaling of HaH; and 9) ethical issues in HaH. Key research issues and questions were identified for each domain.

CONCLUSIONS

While highly evidence-based, unanswered research questions regarding HaH remain, focusing research efforts on the domains identified in this study will serve to improve HaH for all key HaH stakeholders.