Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study

BackgroundArtificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study.ResultsData extraction and manual review steps in the workflow represented significant time commitments for implementation of clinical ML on a prospective, randomized study. Barriers include limited data availability through the standard clinical workflow and commercial products, the need to aggregate data from multiple sources, and logistical challenges from altering the standard clinical workflow to deliver adaptive care.ConclusionsThe SHIELD-RT study was an early randomized controlled study which enabled assessment of barriers to clinical ML implementation, specifically those which leverage the EHR. These challenges build on a growing body of literature and may provide lessons for future healthcare ML adoption.Trial registrationNCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study

ObjectivesClinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.MethodsAn anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.Results59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.ConclusionsRandomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study

Objectives Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.Methods An anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.Results 59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.Conclusions Randomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation

Insurance Coverage and Forgoing Medical Appointments Because of Cost Among Cancer Survivors After 2016

PURPOSE: The uninsured rate began rising after 2016, which some have attributed to health policies undermining aspects of the Affordable Care Act. Our primary objectives were to assess the changes in insurance coverage and forgoing medical care because of cost in cancer survivors from pre-enactment (2016) through postenactment of those policies (2019) and determine whether there were subgroups that were disproportionately affected. METHODS: The 2016-2019 Behavioral Risk Factor Surveillance System surveys were queried for 18- to 64-year-old cancer survivors. Survey-weighted logistic regression was used to assess temporal changes in (1) insurance coverage and (2) forgoing medical appointments because of cost in the preceding 12 months. RESULTS: A total of 62,669 cancer survivors were identified. The percentage of insured cancer survivors decreased from 92.4% in 2016 to 90.4% in 2019 (odds ratio for change in insurance coverage or affordability per one-year increase [OR(year)], 0.92; 95% CI, 0.86 to 0.98; P = .01), translating to 161,000 fewer cancer survivors in the United States with insurance coverage. There were decreases in employer-sponsored insurance coverage (OR(year), 0.89) but increases in Medicaid coverage (OR(year), 1.17) from 2016 to 2019. Forgoing medical appointments because of cost increased from 17.9% in 2016 to 20.0% in 2019 (OR(year), 1.05; 95% CI, 1.01 to 1.1; P = .025), affecting an estimated 169,000 cancer survivors. The greatest changes were observed among individuals with low income, particularly those residing in nonexpansion states. CONCLUSION: Between 2016 and 2019, there were 161,000 fewer cancer survivors in the United States with insurance coverage, and 169,000 forwent medical care because of cost

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PurposePatients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.Patients and methodsDuring this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.ResultsTwice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851).ConclusionIn this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PurposePatients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.Patients and methodsDuring this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.ResultsTwice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851).ConclusionIn this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs

Comparing Outcomes of Oligometastases Treated with Hypofractionated Image-Guided Radiotherapy (HIGRT) with a Simultaneous Integrated Boost (SIB) Technique versus Metastasis Alone: A Multi-Institutional Analysis

Purpose: We previously reported on the clinical outcomes of treating oligometastases with radiation using an elective simultaneous integrated boost technique (SIB), delivering higher doses to known metastases and reduced doses to adjacent bone or nodal basins. Here we compare outcomes of oligometastases receiving radiation targeting metastases alone (MA) versus those treated via an SIB. Methods: Oligometastatic patients with ≤5 active metastases treated with either SIB or MA radiation at two institutions from 2013 to 2019 were analyzed retrospectively for treatment-related toxicity, pain control, and recurrence patterns. Tumor metastasis control (TMC) was defined as an absence of progression in the high dose planning target volume (PTV). Marginal recurrence (MR) was defined as recurrence outside the elective PTV but within the adjacent bone or nodal basin. Distant recurrence (DR) was defined as any recurrence that is not within the PTV or surrounding bone or nodal basin. The outcome rates were estimated using the Kaplan–Meier method and compared between the two techniques using the log-rank test. Results: 101 patients were treated via an SIB to 90 sites (58% nodal and 42% osseous) and via MA radiation to 46 sites (22% nodal and 78% osseous). The median follow-up among surviving patients was 24.6 months (range 1.4–71.0). Of the patients treated to MA, the doses ranged from 18 Gy in one fraction (22%) to 50 Gy in 10 fractions (50%). Most patients treated with an SIB received 50 Gy to the treated metastases and 30 Gy to the elective PTV in 10 fractions (88%). No acute grade ≥3 toxicities occurred in either cohort. Late grade ≥3 toxicity occurred in 3 SIB patients (vocal cord paralysis and two vertebral body compression), all related to the high dose PTV and not the elective volume. There was similar crude pain relief between cohorts. The MR-free survival rate at 2 years was 87% (95% CI: 70%, 95%) in the MA group and 98% (95% CI: 87%, 99%) in the SIB group (p = 0.07). The crude TMC was 89% (41/46) in the MA group and 94% (85/90) in the SIB group. There were no significant differences in DR-free survival (65% (95% CI: 55–74%; p = 0.24)), disease-free survival (60% (95% CI: 40–75%; p = 0.40)), or overall survival (88% (95% CI: 73–95%; p = 0.26)), between the MA and SIB cohorts. Conclusion: Both SIB and MA irradiation of oligometastases achieved high rates of TMC and similar pain control, with a trend towards improved MR-free survival for oligometastases treated with an SIB. Further investigation of this technique with prospective trials is warranted