Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.

PURPOSE: Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation. METHODS: We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre-SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri-COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models. RESULTS: Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri-COVID-19 sample. The algorithm performance was stable across both pre- and peri-COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm. CONCLUSION: Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high-ACU risk patients and enable preventive interventions to avoid ACU.

Health insurance and financial hardship in cancer survivors during the COVID-19 pandemic.

Uninsured or underinsured individuals with cancer are likely to experience financial hardship, including forgoing healthcare or non-healthcare needs such as food, housing, or utilities. This study evaluates the association between health insurance coverage and financial hardship among cancer survivors during the COVID-19 pandemic. This cross-sectional analysis used Patient Advocate Foundation (PAF) survey data from May to July 2020. Cancer survivors who previously received case management or financial aid from PAF self-reported challenges paying for healthcare and non-healthcare needs during the COVID-19 pandemic. Associations between insurance coverage and payment challenges were estimated using Poisson regression with robust standard errors, which allowed for estimation of adjusted relative risks (aRR). Of 1,437 respondents, 74% had annual household incomes <$48,000. Most respondents were enrolled in Medicare (48%), 22% in employer-sponsored insurance, 13% in Medicaid, 6% in an Affordable Care Act (ACA) plan, and 3% were uninsured. Approximately 31% of respondents reported trouble paying for healthcare during the COVID-19 pandemic. Respondents who were uninsured (aRR 2.58, 95% confidence interval [CI] 1.83-3.64), enrolled in an ACA plan (aRR 1.86, 95% CI 1.28-2.72), employer-sponsored insurance (aRR 1.70, 95% CI 1.23-2.34), or Medicare (aRR 1.49, 95% CI 1.09-2.03) had higher risk of trouble paying for healthcare compared to Medicaid enrollees. Challenges paying for non-healthcare needs were reported by 57% of respondents, with 40% reporting trouble paying for food, 31% housing, 28% transportation, and 20% internet. In adjusted models, Medicare and employer-sponsored insurance enrollees were less likely to have difficulties paying for non-healthcare needs compared to Medicaid beneficiaries. Despite 97% of our cancer survivor sample being insured, 31% and 57% reported trouble paying for healthcare and non-healthcare needs during the COVID-19 pandemic, respectively. Greater attention to both medical and non-medical financial burden is needed given the economic pressures of the COVID-19 pandemic.

Determinants of telemedicine adoption among financially distressed patients with cancer during the COVID-19 pandemic: insights from a nationwide study.

PURPOSE: Telemedicine use during the COVID-19 pandemic among financially distressed patients with cancer, with respect to the determinants of adoption and patterns of utilization, has yet to be delineated. We sought to systematically characterize telemedicine utilization in financially distressed patients with cancer during the COVID-19 pandemic. METHODS: We conducted a cross-sectional analysis of nationwide survey data assessing telemedicine use in patients with cancer during the COVID-19 pandemic collected by Patient Advocate Foundation (PAF) in December 2020. Patients were characterized as financially distressed by self-reporting limited financial resources to manage out-of-pocket costs, psychological distress, and/or adaptive coping behaviors. Primary study outcome was telemedicine utilization during the pandemic. Secondary outcomes were telemedicine utilization volume and modality preferences. Multivariable and Poisson regression analyses were used to identify factors associated with telemedicine use. RESULTS: A convenience sample of 627 patients with cancer responded to the PAF survey. Telemedicine adoption during the pandemic was reported by 67% of patients, with most (63%) preferring video visits. Younger age (19-35 age compared to ≥ 75 age) (OR, 6.07; 95% CI, 1.47-25.1) and more comorbidities (≥ 3 comorbidities compared to cancer only) (OR, 1.79; 95% CI, 1.13-2.65) were factors associated with telemedicine adoption. Younger age (19-35 years) (incidence rate ratios [IRR], 1.78; 95% CI, 24-115%) and higher comorbidities (≥ 3) (IRR; 1.36; 95% CI, 20-55%) were factors associated with higher utilization volume. As area deprivation index increased by 10 units, the number of visits decreased by 3% (IRR 1.03, 95% CI, 1.03-1.05). CONCLUSIONS: The rapid adoption of telemedicine may exacerbate existing inequities, particularly among vulnerable financially distressed patients with cancer. Policy-level interventions are needed for the equitable and efficient provision of this service.

Active surveillance of chemotherapy-related symptom burden in ambulatory cancer patients via the implementation of electronic patient-reported outcomes and sensor-enabled vital signs capture: protocol for a decentralised feasibility pilot study.

INTRODUCTION: Remote patient monitoring (RPM) has emerged as a potential avenue for optimising the management of symptoms in patients undergoing chemotherapy. However, RPM is a complex, multilevel intervention with technology, workflow, contextual and patient experience components. The purpose of this pilot study is to determine the feasibility of RPM protocol implementation with respect to decentralised recruitment, patient retention, adherence to reporting recommendations, RPM platform usability and patient experience in ambulatory cancer patients at high risk for chemotherapy-related symptoms. METHODS AND ANALYSIS: This protocol describes a single-arm decentralised feasibility pilot study of technology-enhanced outpatient symptom management system in patients with gastrointestinal and thoracic cancer receiving chemotherapy and cancer care at a single site (MD Anderson Cancer Center, Houston Texas). An anticipated total of 25 patients will be recruited prior to the initiation of chemotherapy and provided with a set of validated questionnaires at enrollment and after our 1-month feasibility pilot trial period. Our intervention entails the self-reporting of symptoms and vital signs via a HIPAA-compliant, secure tablet interface that also enables (1) the provision of self-care materials to patients, (2) generation of threshold alerts to a dedicated call-centre and (3) videoconferencing. Vital sign information (heart rate, blood pressure, pulse, oxygen saturation, weight and temperature) will be captured via Bluetooth-enabled biometric monitoring devices which are integrated with the tablet interface. Protocolised triage and management of symptoms will occur in response to the alerts. Feasibility and acceptability metrics will characterise our recruitment process, protocol adherence, patient retention and usability of the RPM platform. We will also document the perceived effectiveness of our intervention by patients. ETHICS AND DISSEMINATION: This study has been granted approval by the institutional review board of MD Anderson Cancer Center. We anticipate dissemination of our pilot and subsequent effectiveness trial results via presentations at national conferences and peer-reviewed publications in the relevant medical journals. Our results will also be made available to cancer survivors, their caregivers and hospital administration. TRIAL REGISTRATION NUMBER: NCI202107464.

Financial Impacts And Operational Implications Of Private Equity Acquisition Of US Hospitals.

Although private equity acquisition of short-term acute care hospitals purportedly improves efficiency and cost-effectiveness, financial performance after acquisition remains unexamined. We compared changes in the financial performance of 176 hospitals acquired during 2005-14 versus changes in matched control hospitals. Acquisition was associated with a $432 decrease in cost per adjusted discharge and a 1.78-percentage-point increase in operating margin. The majority of acquisitions-134 members of the Hospital Corporation of America, acquired in 2006-were associated with a $559 decrease in cost per adjusted discharge but no change in operating margin. Conversely, non-HCA hospitals exhibited a 3.27-percentage-point increase in operating margin without a concomitant change in cost per adjusted discharge. When we examined markers of hospital capacity, operational efficiency, and costs, we found that private equity acquisition was associated with decreases in total beds, ratio of outpatient to inpatient charges, and staffing (total personnel and nursing full-time equivalents and total full-time equivalents per occupied bed). Therefore, financial performance improved after acquisition, whereas patient throughput and inpatient utilization increased and staffing metrics decreased. Future research is needed to identify any unintended trade-offs with safety and quality.

Applying Lessons from COVID-19 to Cost Centers across the Phases of Surgical Care.

The financial impact of the COVID-19 pandemic has been particularly significant in surgical specialties, with an estimated loss of $22 billion due to deferrals and cancelations of procedures. Evidence suggests that alternative payment models may have reduced the financial impact of COVID-19 for some providers; however, representation of plastic surgery in these models has historically been limited. It is critical for plastic surgeons to understand cost drivers throughout the surgical care episode to design strategies to reduce costs in the wake of the COVID-19 pandemic. In this perspective, we use the American College of Surgeons Five Phases of Surgical Care framework to examine inflationary spending pressures at each stage of the surgical continuum of care. We then highlight cost-containment strategies relevant to plastic and reconstructive surgery within these stages, including those developed before the COVID-19 pandemic, such as bundled payment models and utilization of ambulatory surgery centers, and others expanded during the pandemic, including further use of telemedicine for pre and postoperative visits and expansion of enhanced recovery after surgery pathways and home-based rehabilitation for breast reconstruction. Using innovations from the COVID-19 pandemic can help plastic surgeons further innovate to decrease costs and improve outcomes for patients.

Surgical Outcomes in Cancer Patients Undergoing Elective Surgery After Recovering from Mild-to-Moderate SARS-CoV-2 Infection.

BACKGROUND: An increasing number of patients with cancer diagnoses and prior SARS-CoV-2 infection will require surgical treatment. The objective of this study was to determine whether a history of SARS-CoV-2 infection increases the risk of adverse postoperative events following surgery in patients with cancer. METHODS: This was a propensity-matched cohort study from April 6, 2020 to October 31, 2020 at the UT MD Anderson Cancer Center. Cancer patients were identified who underwent elective surgery after recovering from SARS-CoV-2 infection and matched to controls based on patient, disease, and surgical factors. Primary study outcome was a composite of the following adverse postoperative events that occurred within 30 days of surgery: death, unplanned readmission, pneumonia, cardiac injury, or thromboembolic event. RESULTS: A total of 5682 patients were included for study, and 114 (2.0%) had a prior SARS-CoV-2 infection. The average time from infection to surgery was 52 (range 20-202) days. Compared with matched controls, there was no difference in the rate of adverse postoperative outcome (14.3% vs. 13.4%, p = 1.0). Patients with a SARS-CoV-2-related inpatient admission before surgery had increased odds of postoperative complication (adjusted odds ratio [aOR] 7.4 [1.6-34.3], p = 0.01). CONCLUSIONS: A minimal wait time of 20 days after recovering from minimally symptomatic SARS-CoV-2 infection appears to be safe for cancer patients undergoing low-risk elective surgery. Patients with SARS-CoV-2 infections requiring inpatient treatment were at increased risk for adverse events after surgery. Additional wait time may be required in those with more severe infections.