Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.

OBJECTIVES: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms. MATERIALS AND METHODS: We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring. RESULTS: We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity. DISCUSSION AND CONCLUSION: Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.

Acute hospitalizations and outcomes in Veterans Affairs Hospitals 2011 to 2017.

Hospitals within the Veterans Affairs (VA) health care system exhibited growing use of observation care. It is unknown how this affected VA hospital performance since observation care is not included in acute inpatient measures. To examine changes in VA hospitalization outcomes and whether it was affected by shifting acute inpatient care to observation care. Longitudinal analysis of 986,355 acute hospitalizations and observation stays in 11 states 2011 to 2017. We estimated temporal changes in 30-day mortality, 30-day readmissions, costs, and length of stay (LOS) for all hospitalizations and 6 conditions in adjusted models. Changes in mortality and readmissions were compared including and excluding observation care. A 9% drop in acute hospitalizations was offset by a 157% increase in observation stays 2011 to 2017. A 30-day mortality decreased but readmissions did not when observation stays were included (all P < .05). Mean costs increased modestly; mean LOS was unchanged. There were differences by condition. VA hospital mortality decreased; there was no change in readmissions.

Impact of Referring High-Risk Patients to Intensive Outpatient Primary Care Services: A Propensity Score-Matched Analysis.

BACKGROUND: Many healthcare systems have implemented intensive outpatient primary care programs with the hopes of reducing healthcare costs. OBJECTIVE: The Veterans Health Administration (VHA) piloted primary care intensive management (PIM) for patients at high risk for hospitalization or death, or "high-risk." We evaluated whether a referral model would decrease high-risk patient costs. DESIGN: Retrospective cohort study using a quasi-experimental design comparing 456 high-risk patients referred to PIM from October 2017 to September 2018 to 415 high-risk patients matched on propensity score. PARTICIPANTS: Veterans in the top 10th percentile of risk for 90-day hospitalization or death and recent hospitalization or emergency department (ED) visit. INTERVENTION: PIM consisted of interdisciplinary teams that performed comprehensive assessments, intensive case management, and care coordination services. MAIN OUTCOMES AND MEASURES: Change in VHA and non-VHA outpatient utilization, inpatient admissions, and costs 12 months pre- and post-index date. KEY RESULTS: Of the 456 patients referred to PIM, 301 (66%) enrolled. High-risk patients referred to PIM had a marginal reduction in ED visits (- 0.7; [95% CI - 1.50 to 0.08]; p = 0.08) compared to propensity-matched high-risk patients; overall outpatient costs were similar. High-risk patients referred to PIM had similar number of medical/surgical hospitalizations (- 0.2; [95% CI, - 0.6 to 0.16]; p = 0.2), significant increases in length of stay (6.36; [CI, - 0.01 to 12.72]; p = 0.05), and higher inpatient costs ($22,628, [CI, $3587 to $41,669]; p = 0.02) than those not referred to PIM. CONCLUSIONS AND RELEVANCE: VHA intensive outpatient primary care was associated with higher costs. Referral to intensive case management programs targets the most complex patients and may lead to increased utilization and costs, particularly in an integrated healthcare setting with robust patient-centered medical homes. TRIAL REGISTRATION: PIM 2.0: Patient Aligned Care Team (PACT) Intensive Management (PIM) Project (PIM2). NCT04521816. https://clinicaltrials.gov/study/NCT04521816.

Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Text vs Patient Portal Messaging to Improve Influenza Vaccination Coverage: A Health System-Wide Randomized Clinical Trial.

Importance: Increasing influenza vaccination rates is a public health priority. One method recommended by the US Centers for Disease Control and Prevention and others is for health systems to send reminders nudging patients to be vaccinated. Objective: To evaluate and compare the effect of electronic health record (EHR)-based patient portal reminders vs text message reminders on influenza vaccination rates across a health system. Design, Setting, and Participants: This 3-arm randomized clinical trial was conducted from September 7, 2022, to April 30, 2023, among primary care patients within the University of California, Los Angeles (UCLA) health system. Interventions: Arm 1 received standard of care. The health system sent monthly reminder messages to patients due for an influenza vaccine by portal (arm 2) or text (arm 3). Arm 2 had a 2 × 2 nested design, with fixed vs responsive monthly reminders and preappointment vs no preappointment reminders. Arm 3 had 1 × 2 design, with preappointment vs no preappointment reminders. Preappointment reminders for eligible patients were sent 24 and 48 hours before scheduled primary care visits. Fixed reminders (in October, November, and December) involved identical messages via portal or text. Responsive portal reminders involved a September message asking patients about their plans for vaccination, with a follow-up reminder if the response was affirmative but the patient was not yet vaccinated. Main Outcomes and Measures: The primary outcome was influenza vaccination by April 30, 2023, obtained from the UCLA EHR, including vaccination from pharmacies and other sources. Results: A total of 262 085 patients (mean [SD] age, 45.1 [20.7] years; 237 404 [90.6%] adults; 24 681 [9.4%] children; 149 349 [57.0%] women) in 79 primary care practices were included (87 257 in arm 1, 87 478 in arm 2, and 87 350 in arm 3). At the entire primary care population level, none of the interventions improved influenza vaccination rates. All groups had rates of approximately 47%. There was no statistical or clinically significant improvement following portal vs text, preappointment reminders vs no preappointment reminders (portal and text reminders combined), or responsive vs fixed monthly portal reminders. Conclusions and Relevance: At the population level, neither portal nor text reminders for influenza vaccination were effective. Given that vaccine hesitancy may be a major reason for the lack of impact of portal or text reminders, more intensive interventions by health systems are needed to raise influenza vaccination coverage levels. Trial Registration: ClinicalTrials.gov Identifier: NCT05525494.

Patients with Heart Failure: Internet Use and Mobile Health Perceptions.

BACKGROUND: Heart failure is a complex clinical syndrome noted on approximately one in eight death certificates in the United States. Vital to reducing complications of heart failure and preventing hospital readmissions is adherence to heart failure self-care routines. Mobile health offers promising opportunities for enhancing self-care behaviors by facilitating tracking and timely reminders. OBJECTIVES: We sought to investigate three characteristics of heart failure patients with respect to their heart failure self-care behaviors: (1) internet use to search for heart failure information; (2) familiarity with mobile health apps and devices; and (3) perceptions of using activity trackers or smartwatches to aid in their heart failure self-care. METHODS: Forty-nine heart failure patients were asked about their internet and mobile health usage. The structured interview included questions adapted from the Health Information National Trends Survey. RESULTS: Over 50% of the patients had utilized the internet to search for heart failure information in the past 12 months, experience using health-related apps, and thoughts that an activity tracker or smartwatch could help them manage heart failure. Qualitative analysis of the interviews revealed six themes: trust in their physicians, alternatives to mobile health apps, lack of need for mobile health devices, financial barriers to activity tracker and smartwatch ownership, benefits of tracking and reminders, and uncertainty of their potential due to lack of knowledge. CONCLUSION: Trust in their physicians was a major factor for heart failure patients who reported not searching for health information on the internet. While those who used mobile health technologies found them useful, patients who did not use them were generally unaware of or unknowledgeable about them. Considering patients' preferences for recommendations from their physicians and tendency to search for heart failure information including treatment and management options, patient-provider discussions about mobile health may improve patient knowledge and impact their usage.

Comparative Effectiveness of Psychotherapy vs Antidepressants for Depression in Heart Failure: A Randomized Clinical Trial.

Importance: Heart failure (HF) affects more than 6 million adults in the US and more than 64 million adults worldwide, with 50% prevalence of depression. Patients and clinicians lack information on which interventions are more effective for depression in HF. Objective: To compare the effectiveness of behavioral activation psychotherapy (BA) vs antidepressant medication management (MEDS) on patient-centered outcomes inpatients with HF and depression. Design, Setting, and Participants: This pragmatic randomized comparative effectiveness trial was conducted from 2018 to 2022, including 1-year follow-up, at a not-for-profit academic health system serving more than 2 million people from diverse demographic, socioeconomic, cultural, and geographic backgrounds. Participant included inpatients and outpatients diagnosed with HF and depression, and data were analyzed as intention-to-treat. Data were analyzed from 2022 to 2023. Interventions: BA is an evidence-based manualized treatment for depression, promoting engagement in personalized pleasurable activities selected by patients. MEDS involves the use of an evidence-based collaborative care model with care managers providing coordination with patients, psychiatrists, and primary care physicians to only administer medications. Main Outcomes and Measures: The primary outcome was depressive symptom severity at 6 months, measured using the Patient Health Questionnaire 9-Item (PHQ-9). Secondary outcomes included physical and mental health-related quality of life (HRQOL), measured using the Short-Form 12-Item version 2 (SF-12); heart failure-specific HRQOL, measured using the Kansas City Cardiomyopathy Questionnaire; caregiver burden, measured with the Caregiver Burden Questionnaire for Heart Failure; emergency department visits; readmissions; days hospitalized; and mortality at 3, 6, and 12 months. Results: A total of 416 patients (mean [SD] age, 60.71 [15.61] years; 243 [58.41%] male) were enrolled, with 208 patients randomized to BA and 208 patients randomized to MEDS. At baseline, mean (SD) PHQ-9 scores were 14.54 (3.45) in the BA group and 14.31 (3.60) in the MEDS group; both BA and MEDS recipients experienced nearly 50% reduction in depressive symptoms at 3, 6, and 12 months (eg, mean [SD] score at 12 months: BA, 7.62 (5.73); P < .001; MEDS, 7.98 (6.06); P < .001; between-group P = .55). There was no statistically significant difference between BA and MEDS in the primary outcome of PHQ-9 at 6 months (mean [SD] score, 7.53 [5.74] vs 8.09 [6.06]; P = .88). BA recipients, compared with MEDS recipients, experienced small improvement in physical HRQOL at 6 months (mean [SD] SF-12 physical score: 38.82 [11.09] vs 37.12 [10.99]; P = .04), had fewer ED visits (3 months: 38% [95% CI, 14%-55%] reduction; P = .005; 6 months: 30% [95% CI, 14%-40%] reduction; P = .008; 12 months: 27% [95% CI, 15%-38%] reduction; P = .001), and spent fewer days hospitalized (3 months: 17% [95% CI, 8%-25%] reduction; P = .002; 6 months: 19% [95% CI, 13%-25%] reduction; P = .005; 12 months: 36% [95% CI, 32%-40%] reduction; P = .001). Conclusions and Relevance: In this comparative effectiveness trial of BA and MEDS in patients with HF experiencing depression, both treatments significantly reduced depressive symptoms by nearly 50% with no statistically significant differences between treatments. BA recipients experienced better physical HRQOL, fewer ED visits, and fewer days hospitalized. The study findings suggested that patients with HF could be given the choice between BA or MEDS to ameliorate depression. Trial Registration: ClinicalTrials.gov Identifier: NCT03688100.

Hospital and Patient Factors Affecting Veterans' Hospital Choice.

Veterans enrolled in the Veterans Affairs (VA) health care system gained greater access to non-VA care beginning in 2014. We examined hospital and Veteran characteristics associated with hospital choice. We conducted a longitudinal study of elective hospitalizations 2011 to 2017 in 11 states and modeled patients' choice of VA hospital, large non-VA hospital, or small non-VA hospital in conditional logit models. Patients had higher odds of choosing a hospital with an academic affiliation, better patient experience rating, location closer to them, and a more common hospital type. Patients who were male, racial/ethnic minorities, had higher VA enrollment priority, and had a mental health comorbidity were more likely than other patients to choose a VA hospital than a non-VA hospital. Our findings suggest that patients respond to certain hospital attributes. VA hospitals may need to maintain or achieve high levels of quality and patient experience to attract or retain patients in the future.

Outcomes of Veterans Treated in Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals.

Importance: Many veterans enrolled in the Veterans Affairs (VA) health care system have access to non-VA care through insurance and VA-purchased community care. Prior comparisons of VA and non-VA hospital outcomes have been limited to subpopulations. Objective: To compare outcomes for 6 acute conditions in VA and non-VA hospitals for younger and older veterans using VA and all-payer discharge data. Design, Setting, and Participants: This cohort study used a repeated cross-sectional analysis of hospitalization records for acute myocardial infarction (AMI), coronary artery bypass graft (CABG), gastrointestinal (GI) hemorrhage, heart failure (HF), pneumonia, and stroke. Participants included VA enrollees from 11 states at VA and non-VA hospitals from 2012 to 2017. Analysis was conducted from July 1, 2022, to October 18, 2023. Exposures: Treatment in VA or non-VA hospital. Main Outcome and Measures: Thirty-day mortality, 30-day readmission, length of stay (LOS), and costs. Average treatment outcomes of VA hospitals were estimated using inverse probability weighted regression adjustment to account for selection into hospitals. Models were stratified by veterans' age (aged less than 65 years and aged 65 years and older). Results: There was a total of 593 578 hospitalizations and 414 861 patients with mean (SD) age 75 (12) years, 405 602 males (98%), 442 297 hospitalizations of non-Hispanic White individuals (75%) and 73 155 hospitalizations of non-Hispanic Black individuals (12%) overall. VA hospitalizations had a lower probability of 30-day mortality for HF (age ≥65 years, -0.02 [95% CI, -0.03 to -0.01]) and stroke (age <65 years, -0.03 [95% CI, -0.05 to -0.02]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.03]). VA hospitalizations had a lower probability of 30-day readmission for CABG (age <65 years, -0.04 [95% CI, -0.06 to -0.01]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.02]), GI hemorrhage (age <65 years, -0.04 [95% CI, -0.06 to -0.03]), HF (age <65 years, -0.05 [95% CI, -0.07 to -0.03]), pneumonia (age <65 years, -0.04 [95% CI, -0.06 to -0.03]; age ≥65 years, -0.03 [95% CI, -0.04 to -0.02]), and stroke (age <65 years, -0.11 [95% CI, -0.13 to -0.09]; age ≥65 years, -0.13 [95% CI, -0.16 to -0.10]) but higher probability of readmission for AMI (age <65 years, 0.04 [95% CI, 0.01 to 0.06]). VA hospitalizations had a longer mean LOS and higher costs for all conditions, except AMI and stroke in younger patients. Conclusions and Relevance: In this cohort study of veterans, VA hospitalizations had lower mortality for HF and stroke and lower readmissions, longer LOS, and higher costs for most conditions compared with non-VA hospitalizations with differences by condition and age group. There were tradeoffs between better outcomes and higher resource use in VA hospitals for some conditions.

Differences in use of Veterans Health Administration and non-Veterans Health Administration hospitals by rural and urban Veterans after access expansions.

PURPOSE: To examine changes in rural and urban Veterans' utilization of acute inpatient care in Veterans Health Administration (VHA) and non-VHA hospitals following access expansion from the Veterans Choice Act, which expanded eligibility for VHA-paid community hospitalization. METHODS: Using repeated cross-sectional data of VHA enrollees' hospitalizations in 9 states (AZ, CA, CT, FL, LA, MA, NY, PA, and SC) between 2012 and 2017, we compared rural and urban Veterans' probability of admission in VHA and non-VHA hospitals by payer over time for elective and nonelective hospitalizations using multinomial logistic regression to adjust for patient-level sociodemographic features. We also used generalized linear models to compare rural and urban Veterans' travel distances to hospitals. FINDINGS: Over time, the probability of VHA-paid community hospitalization increased more for rural Veterans than urban Veterans. For elective inpatient care, rural Veterans' probability of VHA-paid admission increased from 2.9% (95% CI 2.6%-3.2%) in 2012 to 6.5% (95% CI 5.8%-7.1%) in 2017. These changes were associated with a temporal trend that preceded and continued after the implementation of the Veterans Choice Act. Overall travel distances to hospitalizations were similar over time; however, the mean distance traveled decreased from 39.2 miles (95% CI 35.1-43.3) in 2012 to 32.3 miles (95% CI 30.2-34.4) in 2017 for rural Veterans receiving elective inpatient care in VHA-paid hospitals. CONCLUSIONS: Despite limited access to rural hospitals, these data demonstrate an increase in rural Veterans' use of non-VHA hospitals for acute inpatient care and a small reduction in distance traveled to elective inpatient services.

Geographic Variation in the Quality of Heart Failure Care Among U.S. Veterans.

BACKGROUND: The burden of heart failure is growing. Guideline-directed medical therapies (GDMT) reduce adverse outcomes in heart failure with reduced ejection fraction (HFrEF). Whether there is geographic variation in HFrEF quality of care is not well described. OBJECTIVES: This study evaluated variation nationally for prescription of GDMT within the Veterans Health Administration. METHODS: A cohort of Veterans with HFrEF had their address linked to hospital referral regions (HRRs). GDMT prescription was defined using pharmacy data between July 1, 2020, and July 1, 2021. Within HRRs, we calculated the percentage of Veterans prescribed GDMT and a composite GDMT z-score. National choropleth maps were created to evaluate prescription variation. Associations between GDMT performance and demographic characteristics were evaluated using linear regression. RESULTS: Maps demonstrated significant variation in the HRR composite score and GDMT prescriptions. Within HRRs, the prescription of beta-blockers to Veterans was highest with a median of 80% (IQR: 77.3%-82.2%) followed by angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitors (69.3%; IQR: 66.4%-72.1%), sodium-glucose cotransporter-2 inhibitors (10.3%; IQR: 7.7%-12.8%), mineralocorticoid receptor antagonists (29.2%; IQR: 25.8%-33.9%), and angiotensin receptor-neprilysin inhibitors (12.2%; IQR: 8.6%-15.3%). HRR composite GDMT z-scores were inversely associated with the HRR median Gini coefficient (R = -0.13; P = 0.0218) and the percentage of low-income residents (R = -0.117; P = 0.0413). CONCLUSIONS: Wide geographic differences exist for HFrEF care. Targeted strategies may be required to increase GDMT prescription for Veterans in lower-performing regions, including those affected by income inequality and poverty.

Smoking Cessation, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology.

Although the harmful effects of smoking after a cancer diagnosis have been clearly demonstrated, many patients continue to smoke cigarettes during treatment and beyond. The NCCN Guidelines for Smoking Cessation emphasize the importance of smoking cessation in all patients with cancer and seek to establish evidence-based recommendations tailored to the unique needs and concerns of patients with cancer. The recommendations contained herein describe interventions for cessation of all combustible tobacco products (eg, cigarettes, cigars, hookah), including smokeless tobacco products. However, recommendations are based on studies of cigarette smoking. The NCCN Smoking Cessation Panel recommends that treatment plans for all patients with cancer who smoke include the following 3 tenets that should be done concurrently: (1) evidence-based motivational strategies and behavior therapy (counseling), which can be brief; (2) evidence-based pharmacotherapy; and (3) close follow-up with retreatment as needed.

Evaluation of behavioral economic strategies to raise influenza vaccination rates across a health system: Results from a randomized clinical trial.

Influenza vaccination rates are low. Working with a large US health system, we evaluated three health system-wide interventions using the electronic health record's patient portal to improve influenza vaccination rates. We performed a two-arm RCT with a nested factorial design within the treatment arm, randomizing patients to usual-care control (no portal interventions) or to one or more portal interventions. We included all patients within this health system during the 2020-2021 influenza vaccination season, which overlapped with the COVID-19 pandemic. Through the patient portal, we simultaneously tested: pre-commitment messages (sent September 2020, asking patients to commit to a vaccination); monthly portal reminders (October - December 2020), direct appointment scheduling (patients could self-schedule influenza vaccination at multiple sites); and pre-appointment reminder messages (sent before scheduled primary care appointments, reminding patients about influenza vaccination). The main outcome measure was receipt of influenza vaccine (10/01/2020-03/31/2021). We randomized 213,773 patients (196,070 adults ≥18 years, 17,703 children). Influenza vaccination rates overall were low (39.0%). Vaccination rates for study arms did not differ: Control (38.9%), pre-commitment vs no pre-commitment (39.2%/38.9%), direct appointment scheduling yes/no (39.1%/39.1%), pre-appointment reminders yes/no (39.1%/39.1%); p > 0.017 for all comparisons (p value cut-off adjusted for multiple comparisons). After adjusting for age, gender, insurance, race, ethnicity, and prior influenza vaccination, none of the interventions increased vaccination rates. We conclude that patient portal interventions to remind patients to receive influenza vaccine during the COVID-19 pandemic did not raise influenza immunization rates. More intensive or tailored interventions are needed beyond portal innovations to increase influenza vaccination.

Postdischarge Noninvasive Telemonitoring and Nurse Telephone Coaching Improve Outcomes in Heart Failure Patients With High Burden of Comorbidity.

BACKGROUND: Noninvasive telemonitoring and nurse telephone coaching (NTM-NTC) is a promising postdischarge strategy in heart failure (HF). Comorbid conditions and disease burden influence health outcomes in HF, but how comorbidity burden modulates the effectiveness of NTM-NTC is unknown. This study aims to identify patients with HF who may benefit from postdischarge NTM-NTC based on their burden of comorbidity. METHODS AND RESULTS: In the Better Effectiveness After Transition - Heart Failure trial, patients hospitalized for acute decompensated HF were randomized to postdischarge NTM-NTC or usual care. In this secondary analysis of 1313 patients with complete data, comorbidity burden was assessed by scoring complication and coexisting diagnoses from index admissions. Clinical outcomes included 30-day and 180-day readmissions, mortality, days alive, and combined days alive and out of the hospital. Patients had a mean of 5.7 comorbidities and were stratified into low (0-2), moderate (3-8), and high comorbidity (≥9) subgroups. Increased comorbidity burden was associated with worse outcomes. NTM-NTC was not associated with readmission rates in any comorbidity subgroup. Among high comorbidity patients, NTM-NTC was associated with significantly lower mortality at 30 days (hazard ratio 0.25, 95% confidence interval 0.07-0.90) and 180 days (hazard ratio 0.51, 95% confidence interval 0.27-0.98), as well as more days alive (160.1 vs 140.3, P = .029) and days alive out of the hospital (152.0 vs 133.2, P = .044) compared with usual care. CONCLUSIONS: Postdischarge NTM-NTC improved survival among patients with HF with a high comorbidity burden. Comorbidity burden may be useful for identifying patients likely to benefit from this management strategy.

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.

BACKGROUND: Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients. RESEARCH QUESTION: Can a predictive model be developed to accurately identify patients at high risk of PRF? STUDY DESIGN AND METHODS: In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021). Our primary outcome was PRF, defined as mechanical ventilation after surgery of > 48 h. Predictors of interest included demographics, comorbidities, and intraoperative factors. We used logistic regression to build a predictive model and the least absolute shrinkage and selection operator procedure to select variables and to estimate model coefficients. We evaluated model performance using optimism-corrected area under the receiver operating curve and area under the precision-recall curve and calculated sensitivity, specificity, positive and negative predictive values, and Brier scores. RESULTS: Two hundred twenty-five patients (0.94%) demonstrated PRF. The 18-variable predictive model included: operations on the cardiovascular, nervous, digestive, urinary, or musculoskeletal system; surgical specialty orthopedic (nonspine); Medicare or Medicaid (as the primary payer); race unknown; American Society of Anesthesiologists class ≥ III; BMI of 30 to 34.9 kg/m2; anesthesia duration (per hour); net fluid at end of the operation (per liter); median intraoperative FIO2, end title CO2, heart rate, and tidal volume; and intraoperative vasopressor medications. The optimism-corrected area under the receiver operating curve was 0.835 (95% CI,0.808-0.862) and the area under the precision-recall curve was 0.156 (95% CI, 0.105-0.203). INTERPRETATION: This single-center proof-of-concept study demonstrated that a structured query language extract, transform, and load process, based on readily available patient and intraoperative variables, can be used to develop a prediction model for PRF. This PRF prediction model is scalable for multicenter research. Clinical applications include decision support to guide postoperative level of care admission and treatment decisions.

Training the next generation of learning health system scientists.

Introduction: The learning health system (LHS) aligns science, informatics, incentives, stakeholders, and culture for continuous improvement and innovation. The Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute designed a K12 initiative to grow the number of LHS scientists. We describe approaches developed by 11 funded centers of excellence (COEs) to promote partnerships between scholars and health system leaders and to provide mentored research training. Methods: Since 2018, the COEs have enlisted faculty, secured institutional resources, partnered with health systems, developed and implemented curricula, recruited scholars, and provided mentored training. Program directors for each COE provided descriptive data on program context, scholar characteristics, stakeholder engagement, scholar experiences with health system partnerships, roles following program completion, and key training challenges. Results: To date, the 11 COEs have partnered with health systems to train 110 scholars. Nine (82%) programs partner with a Veterans Affairs health system and 9 (82%) partner with safety net providers. Clinically trained scholars (n = 87; 79%) include 70 physicians and 17 scholars in other clinical disciplines. Non-clinicians (n = 29; 26%) represent diverse fields, dominated by population health sciences. Stakeholder engagement helps scholars understand health system and patient/family needs and priorities, enabling opportunities to conduct embedded research, improve outcomes, and grow skills in translating research methods and findings into practice. Challenges include supporting scholars through roadblocks that threaten to derail projects during their limited program time, ranging from delays in access to data to COVID-19-related impediments and shifts in organizational priorities. Conclusions: Four years into this novel training program, there is evidence of scholars' accomplishments, both in traditional academic terms and in terms of moving along career trajectories that hold the potential to lead and accelerate transformational health system change. Future LHS training efforts should focus on sustainability, including organizational support for scholar activities.

Health Care Access Expansions and Use of Veterans Affairs and Other Hospitals by Veterans.

This cohort study examines changes in the use of Veterans Affairs (VA) and non-VA hospitals by VA enrollees and mortality associated with these policies.

The Association of Medical Preoperative Evaluation Using Clinical Video Telehealth With Hospital Length of Stay: Descriptive Analysis.

BACKGROUND: Preoperative medical evaluation serves to identify risk factors and optimize patients before surgery. Providing a telehealth option in the perioperative setting has played a significant role in reducing barriers to quality perioperative health care. OBJECTIVE: We aimed to evaluate how telemedicine preoperative evaluations using Clinical Video Telehealth (CVT) impact hospital length of stay. METHODS: We performed a retrospective chart review between 2016 and 2017 of adult patients who underwent evaluations in our hospitalist-run preoperative medicine clinic. Patients seen in our preoperative CVT program were compared to patients seen in person to evaluate the association of visit type (preoperative CVT versus in-person evaluation) with hospital length of stay, defined as hospital stay from postoperative day 0 to discharge. There were 62 patients included in this retrospective study. RESULTS: The adjusted incidence rate ratio (IRR) for hospital length of stay was significantly shorter in patients who underwent preoperative CVT compared to an in-person visit (IRR 0.52, 95% CI 0.29-0.92, P=.02). CONCLUSIONS: After adjusting for age and comorbidities, we show that preoperative telemedicine in the perioperative setting is associated with a shorter hospital length of stay compared to in-person visits. This suggests that telemedicine can play a viable role in this clinical setting.

Outcomes and risk factors for delayed-onset postoperative respiratory failure: a multi-center case-control study by the University of California Critical Care Research Collaborative (UC3RC).

BACKGROUND: Few interventions are known to reduce the incidence of respiratory failure that occurs following elective surgery (postoperative respiratory failure; PRF). We previously reported risk factors associated with PRF that occurs within the first 5 days after elective surgery (early PRF; E-PRF); however, PRF that occurs six or more days after elective surgery (late PRF; L-PRF) likely represents a different entity. We hypothesized that L-PRF would be associated with worse outcomes and different risk factors than E-PRF. METHODS: This was a retrospective matched case-control study of 59,073 consecutive adult patients admitted for elective non-cardiac and non-pulmonary surgical procedures at one of five University of California academic medical centers between October 2012 and September 2015. We identified patients with L-PRF, confirmed by surgeon and intensivist subject matter expert review, and matched them 1:1 to patients who did not develop PRF (No-PRF) based on hospital, age, and surgical procedure. We then analyzed risk factors and outcomes associated with L-PRF compared to E-PRF and No-PRF. RESULTS: Among 95 patients with L-PRF, 50.5% were female, 71.6% white, 27.4% Hispanic, and 53.7% Medicare recipients; the median age was 63 years (IQR 56, 70). Compared to 95 matched patients with No-PRF and 319 patients who developed E-PRF, L-PRF was associated with higher morbidity and mortality, longer hospital and intensive care unit length of stay, and increased costs. Compared to No-PRF, factors associated with L-PRF included: preexisiting neurologic disease (OR 4.36, 95% CI 1.81-10.46), anesthesia duration per hour (OR 1.22, 95% CI 1.04-1.44), and maximum intraoperative peak inspiratory pressure per cm H20 (OR 1.14, 95% CI 1.06-1.22). CONCLUSIONS: We identified that pre-existing neurologic disease, longer duration of anesthesia, and greater maximum intraoperative peak inspiratory pressures were associated with respiratory failure that developed six or more days after elective surgery in adult patients (L-PRF). Interventions targeting these factors may be worthy of future evaluation.