Diverse patient trajectories during cytotoxic chemotherapy: Capturing longitudinal patient-reported outcomes

Electronic Patient-Reported Outcome-Driven Symptom Management by Oncology Pharmacists in a Majority-Minority Population: An Implementation Study

PURPOSE

There is a lack of systematic solutions to manage supportive care issues in racial/ethnic minorities (REM) receiving treatment for cancer. We developed and implemented an electronic patient-reported outcome (ePRO)-driven symptom management tool led by oncology pharmacists in a majority-minority cancer center located in Southern California. This study was designed to evaluate the implementation outcomes of our multilevel intervention.

METHODS

This was a prospective, pragmatic, implementation study conducted between July 2021 and June 2023. Newly diagnosed adult patients with cancer receiving intravenous anticancer therapies completed symptom screening using ePRO that consists of the Patient-Reported Outcomes Measurement Information System measures at each infusion visit during the study. ePRO results were presented to an oncologist pharmacist for personalized symptom management and treatment counseling. The RE-AIM framework was used to guide implementation outcomes. Differences in symptom trajectories and clinical outcomes between groups were tested using generalized estimating equations.

RESULTS

We screened 388 patients of whom 250 were enrolled (acceptance rate: 64.4%), with 564 assessments being completed. The sample consisted of non-Hispanic White (NHW, 42.4%), Hispanic/Latinx (H/L, 30.8%), and non-Hispanic Asian (20.4%), with one (21.6%) of five participants preferring speaking Spanish. Compared with NHW, H/L participants had greater odds of reporting mild to severe pain interference (odds ratio [OR], 1.91 [95% CI, 1.18 to 3.08]; P = .008) and nausea and vomiting (OR, 2.08 [95% CI, 1.21 to 3.58]; P = .008), and higher rates of urgent care utilization (OR, 1.92 [95% CI, 1.04 to 3.61]; P = .04) within 30 days. Nausea and vomiting (n = 131, 23.2%), pain (n = 91, 16.1%), and fatigue (n = 72, 12.8%) were most likely to be intervened, with 90% of the participants expressing satisfaction across all visits.

CONCLUSION

Our multilevel ePRO-driven intervention led by oncology pharmacists helps facilitate symptom assessments and management and potentially reduce health disparities among REM.

Mortality Risks Associated with Depression in Men with Prostate Cancer

BACKGROUND

Men diagnosed with prostate cancer (PC) have an increased risk of depression; however, it is unclear to what extent depression affects long-term survival. A better understanding of such effects is needed to improve long-term care and outcomes for men with PC.

OBJECTIVE

To determine the associations between major depression and mortality in a national cohort of men with PC.

DESIGN, SETTING, AND PARTICIPANTS

A national cohort study was conducted of all 180 189 men diagnosed with PC in Sweden during 1998-2017. Subsequent diagnoses of major depression were ascertained from nationwide outpatient and inpatient records through 2018.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Deaths were identified from nationwide records through 2018. Cox regression was used to compute hazard ratios (HRs) for all-cause mortality associated with major depression, adjusting for sociodemographic factors and comorbidities. Subanalyses assessed differences by PC treatment during 2005-2017. PC-specific mortality was examined using competing risks models.

RESULTS AND LIMITATIONS

In 1.3 million person-years of follow-up, 16 134 (9%) men with PC were diagnosed with major depression and 65 643 (36%) men died. After adjusting for sociodemographic factors and comorbidities, major depression was associated with significantly higher all-cause mortality in men with high-risk PC (HR, 1.50; 95% confidence interval [CI], 1.44-1.55) or low- or intermediate-risk PC (1.64; 1.56-1.71). These risks were elevated regardless of PC treatment or age at PC diagnosis, except for youngest men (<55 yr) in whom the risks were nonsignificant. Major depression was also associated with increased PC-specific mortality in men with either high-risk PC (HR, 1.35; 95% CI, 1.28-1.43) or low- or intermediate-risk PC (1.42; 1.27-1.59). This study was limited to Sweden and will need replication in other countries when feasible.

CONCLUSIONS

In this national cohort of men with PC, major depression was associated with ∼50% higher all-cause mortality. Men with PC need timely detection and treatment of depression to support their long-term outcomes and survival.

PATIENT SUMMARY

In this report, we examined the effects of depression on survival in men with prostate cancer. We found that among all men with prostate cancer, those who developed depression had a 50% higher risk of dying than those without depression. Men with prostate cancer need close monitoring for the detection and treatment of depression to improve their long-term health outcomes.

Impact of systemic cancer treatment on quality of life and mental well-being: a comparative analysis of patients with localized and advanced cancer

INTRODUCTION

This study investigated the impact of systemic cancer therapy on the quality of life, mental well-being, and life satisfaction of cancer patients.

METHODS

This prospective study was promoted by the Spanish Society of Medical Oncology (SEOM) and enrolled patients with localized, resected, or unresectable advanced cancer from 15 Spanish medical oncology departments. Patients completed surveys on quality of life (EORTC-QoL-QLQ-C30), psychological distress (BSI-18) and life satisfaction (SWLS) before and after systemic cancer treatment.

RESULTS

The study involved 1807 patients, 944 (52%) having resected, localized cancer, and 863 with unresectable advanced cancer. The mean age was 60 years, and 53% were female. The most common types of localized cancer were colorectal (43%) and breast (38%), while bronchopulmonary (32%), non-colorectal digestive (23%), and colorectal (15%) were the most frequent among those with advanced cancer. Before systemic treatment, patients with advanced cancer had poorer scores than those with localized cancer on physical, role, emotional, cognitive, social limitations, symptoms, psychological distress, and life satisfaction (all p < 0.001), but there were no differences in financial hardship. Patients with localized cancer had greater life satisfaction and better mental well-being than those with advanced cancer before systemic treatment (p < 0.001). After treatment, patients with localized cancer experienced worsening of all scales, symptoms, and mental well-being (p < 0.001), while patients with advanced disease had a minor decline in quality of life. The impact on quality of life was greater on all dimensions except economic hardship and was independent of age, cancer location, and performance status in participants with resected disease after adjuvant chemotherapy.

CONCLUSION

In conclusion, our study highlights that systemic cancer treatment can improve quality of life in patients with advanced cancer, while adjuvant treatments for localized disease may have a negative impact on quality of life and psychological well-being. Therefore, treatment decisions should be carefully evaluated on an individual basis.

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

Patient-reported outcomes and quality of life in PMM2-CDG

Patient-reported outcomes (PROs) measure important aspects of disease burden, however they have received limited attention in the care of patients with Congenital Disorders of Glycosylation (CDG). We evaluated the PROs and correlation between clinical disease severity scoring and reported quality of life (QoL) in a PMM2-CDG patient cohort. Twenty-five patients with diagnosis of PMM2-CDG were enrolled as part of the Frontiers in Congenital Disorders of Glycosylation Consortium (FCDGC) natural history study. Patient- Reported Outcomes Measurement Information System (PROMIS) was completed by caregivers to assess health-related QoL. Clinical disease severity was scored by medical providers using the Nijmegen Progression CDG Rating Scale (NPCRS). The domains such as physical activity, strength impact, upper extremity, physical mobility, and a satisfaction in social roles (peer relationships) were found to be the most affected in the PMM2-CDG population compared to US general population. We found a strong correlation between NPCRS 1 (current functional ability) and three out of ten PROMIS subscales. NPCRS 2 (laboratory and organ function) and NPCRS 3 (neurological involvement) did not correlate with PROMIS. Mental health domains, such as anxiety, were positively correlated with depressive symptoms (r = 0.76, p = 0.004), fatigue (r = 0.67, p = 0.04). Surprisingly, patients with severely affected physical mobility showed low anxiety scores according to PROMIS (inverse correlation, r = -0.74, p = 0.005). Additionally, there was a positive correlation between upper extremity and physical mobility (r = 0.75, p = 002). Here, we found that PROMIS is an informative additional tool to measure CDG disease burden, which could be used as clinical trial outcome measures. The addition of PROMIS to clinical follow-up could help improve the quality of care for PMM2-CDG by facilitating a holistic approach for clinical decision-making. SYNOPSIS: We recommend PROMIS as an informative tool to measure disease burden in PMM2-CDG in addition to traditional CDG disease severity scores.

Machine Learning Applied to Electronic Health Records: Identification of Chemotherapy Patients at High Risk for Preventable Emergency Department Visits and Hospital Admissions

PURPOSE

Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data.

METHODS

Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve.

RESULTS

Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients.

CONCLUSION

Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.