Optimizing inpatient care for lung cancer patients with immune checkpoint inhibitor-related pneumonitis using a clinical care pathway algorithm.

PURPOSE: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a condition associated with high mortality, necessitating prompt recognition and treatment initiation. This study aimed to assess the impact of implementing a clinical care pathway algorithm on reducing the time to treatment for ICI-P. METHODS: Patients with lung cancer and suspected ICI-P were enrolled, and a multimodal intervention promoting algorithm use was implemented in two phases. Pre- and post-intervention analyses were conducted to evaluate the primary outcome of time from ICI-P diagnosis to treatment initiation. RESULTS: Of the 82 patients admitted with suspected ICI-P, 73.17% were confirmed to have ICI-P, predominantly associated with non-small cell lung cancer (91.67%) and stage IV disease (95%). Pembrolizumab was the most commonly used immune checkpoint inhibitor (55%). The mean times to treatment were 2.37 days in the pre-intervention phase, 3.07 days (p = 0.46), and 1.27 days (p = 0.40) in the post-intervention phases 1 and 2, respectively. Utilization of the immunotoxicity order set significantly increased from 0 to 27.27% (p = 0.04) after phase 2. While there were no significant changes in ICU admissions or inpatient mortality, outpatient pulmonology follow-ups increased statistically significantly, demonstrating enhanced continuity of care. The overall mortality for patients with ICI-P was 22%, underscoring the urgency of optimizing management strategies. Notably, all patients discharged on high-dose corticosteroids received appropriate gastrointestinal prophylaxis and prophylaxis against Pneumocystis jirovecii pneumonia infections at the end of phase 2. CONCLUSION: Implementing a clinical care pathway algorithm for managing severe ICI-P in hospitalized lung cancer patients standardizes practices, reducing variability in management.

Optimizing Inpatient Care for Lung Cancer Patients with Immune Checkpoint Inhibitor- Related Pneumonitis Using a Clinical Care Pathway Algorithm.

Purpose: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a condition associated with high mortality, necessitating prompt recognition and treatment initiation. This study aimed to assess the impact of implementing a clinical care pathway algorithm on reducing the time to treatment for ICI-P. Methods: Patients with lung cancer and suspected ICI-P were enrolled, and a multi-modal intervention promoting algorithm use was implemented in two phases. Pre- and post-intervention analyses were conducted to evaluate the primary outcome of time from ICI-P diagnosis to treatment initiation. Results: Of the 82 patients admitted with suspected ICI-P, 73.17% were confirmed to have ICI-P, predominantly associated with non-small cell lung cancer (91.67%) and stage IV disease (95%). Pembrolizumab was the most commonly used immune checkpoint inhibitor (55%). The mean times to treatment were 2.37 days in the pre-intervention phase and, 3.07 days (p=0.46), and 1.27 days (p=0.40) in the post-intervention phases 1 and 2, respectively. Utilization of the immunotoxicity order set significantly increased from 0% to 27.27% (p = 0.04) after phase 2. While there were no significant changes in ICU admissions or inpatient mortality, outpatient pulmonology follow-ups increased statistically significantly, demonstrating enhanced continuity of care. The overall mortality for patients with ICI-P was 22%, underscoring the urgency of optimizing management strategies. Notably, all patients discharged on high-dose corticosteroids received appropriate gastrointestinal prophylaxis and prophylaxis against Pneumocystis jirovecii pneumonia infections at the end of phase 2. Conclusion: Implementing a clinical care pathway algorithm for ICI-P management standardizes care practices and enhances patient outcomes, underscoring the importance of structured approaches.

Comparison of LACE and HOSPITAL Readmission Risk Scores for CMS Target and Nontarget Conditions.

This study evaluated the utility and performance of the LACE index and HOSPITAL score with consideration of the type of diagnoses and assessed the accuracy of these models for predicting readmission risks in patient cohorts from 2 large academic medical centers. Admissions to 2 hospitals from 2011 to 2015, derived from the Vizient Clinical Data Base and regional health information exchange, were included in this study (291 886 encounters). Models were assessed using Bayesian information criterion and area under the receiver operating characteristic curve. They were compared in CMS diagnosis-based cohorts and in 2 non-CMS cancer diagnosis-based cohorts. Overall, both models for readmission risk performed well, with LACE performing slightly better (area under the receiver operating characteristic curve 0.73 versus 0.69; P ≤ 0.001). HOSPITAL consistently outperformed LACE among 4 CMS target diagnoses, lung cancer, and colon cancer. Both LACE and HOSPITAL predict readmission risks well in the overall population, but performance varies by salient, diagnosis-based risk factors.

How Leading Hospitals Operationalize Evidence-Based Readmission Reduction Strategies: A Mixed-Methods Comparative Study Using Systematic Review and Survey Design.

Although various interventions targeted at reducing hospital readmissions have been identified in the literature, little is known about actual operationalization of such evidence-based interventions. This study conducted a systematic review and a survey of key informants in 2 leading hospitals, Houston Methodist (HM) and MD Anderson Cancer Center (MDACC), to compare and contrast the most cited evidence-based interventions in the current literature with interventions reported by those hospitals. The authors found that both hospitals followed evidence-based practices reported as successful in the literature. Both hospitals have implemented interventions for inpatient settings, and the timing of interventions was very similar. Major implementation differences observed for post-discharge interventions focused on collaboration. It also was found that HM was more likely than MDACC to use medication reconciliation in outpatient (P = .018) and discharge planning for community/home patients (P = .032). Results will provide hospital professionals with insights for implementing the most effective interventions to reduce readmissions.