Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial.

PURPOSE: Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team. METHODS: This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice. We enrolled patients with stage IV GI and lung cancers undergoing chemotherapy. Over 6 months, patients in two intervention arms received PROStep-weekly text message-based symptom surveys and passive activity monitoring using a wearable accelerometer. PGHD were summarized in dashboards given to patients' oncology team before appointments. One intervention arm received an additional text-based active choice prompt to discuss worsening symptoms or functional status with their clinician. Control patients did not receive PROStep. The coprimary outcomes patient perceptions of oncology team symptom and functional understanding at 6 months were measured on a 1-5 Likert scale (5 = high understanding). RESULTS: One hundred eight patients enrolled: 55% male, 81% White, and 77% had GI cancers. Patient-reported clinician understanding did not differ between control and intervention arms for symptoms (4.5 v 4.5; P = .87) or functional status (4.5 v 4.3; P = .31). In the intervention arms, combined patient adherence to weekly symptom reports and daily activity monitoring was 64% and 53%, respectively. Intervention patients in the PROStep versus PROStep + active choice arms reported low burden from wearing the accelerometer (mean burden [standard deviation], 2.7 [1.3] v 2.1 [1.3]; P = .15) and completing surveys (2.1 [1.2] v 1.9 [1.3]; P = .44). CONCLUSION: Patients receiving PROStep reported high understanding of symptoms and functional status from their oncology team, although this did not differ from controls.

Management of Prostate Cancer in Older Adults.

The majority of men with prostate cancer are diagnosed when they are older than 65 years; however, clinical trial participants are disproportionately younger and more fit than the real-world population treated in typical clinical practices. It is, therefore, unknown whether the optimal approach to prostate cancer treatment is the same for older men as it is for younger and/or more fit men. Short screening tools can be used to efficiently assess frailty, functional status, life expectancy, and treatment toxicity risk. These risk assessment tools allow for targeted interventions to increase a patient's reserve and improve treatment tolerance, potentially allowing more men to experience the benefit of the significant recent treatment advances in prostate cancer. Treatment plans should also take into consideration each patient's individual goals and values considered within their overall health and social context to reduce barriers to care. In this review, we will discuss evidence-based risk assessment and decision tools for older men with prostate cancer, highlight intervention strategies to improve treatment tolerance, and contextualize these tools within the current treatment landscape for prostate cancer.

On-Site Nurse-Led Cancer Genetics Program Increases Cancer Genetic Testing Completion in Black Veterans.

PURPOSE: Telegenetics services can expand access to guideline-recommended cancer genetic testing. However, access is often not distributed equitably to all races and ethnicities. We evaluated the impact of an on-site nurse-led cancer genetics service in a diverse Veterans Affairs Medical Center (VAMC) oncology clinic on likelihood of germline testing (GT) completion. METHODS: We conducted an observational retrospective cohort study of patients who were referred for cancer genetics services at the Philadelphia VAMC between October 1, 2020, and February 28, 2022. We evaluated the association between genetics service (on-site v telegenetics) and likelihood of GT completion in a subcohort of new consults, excluding patients with prior consults and those referred for known history of germline mutations. RESULTS: A total of 238 Veterans, including 108 (45%) seen on site, were identified for cancer genetics services during the study period, with the majority referred for a personal (65%) or family (26%) history of cancer. In the subcohort of new consults, 121 Veterans (54% self-identified race/ethnicity [SIRE]-Black), including 60 (50%) seen on site, were included in the analysis of germline genetic testing completion. In a univariate analysis, patients who were seen by the on-site genetics service had 3.2-fold higher likelihood of completing GT (relative risk, 3.22; 95% CI, 1.89 to 5.48) compared with the telegenetics service. In multivariable regression analysis, the on-site genetics service was associated with higher likelihood of GT completion, but this association was only statistically significant in SIRE-Black compared with SIRE-White Veterans (adjusted RR, 4.78; 95% CI, 1.53 to 14.96; P < .001; P-interaction of race × genetics service = .016). CONCLUSION: An on-site nurse-led cancer genetics service embedded in a VAMC Oncology practice was associated with higher likelihood of germline genetic testing completion than a telegenetics service among self-identified Black Veterans.

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.

PURPOSE: Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer. METHODS: We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice. RESULTS: Among 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO v EHR v PRO: area under the curve [AUC] 0.86 [0.85-0.87] v 0.82 [0.81-0.83] v 0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90] v 0.86 [0.85-0.88] v 0.77 [0.76-0.79]) practices. CONCLUSION: Routinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.

Trends in Medically Integrated Dispensing Among Oncology Practices.

PURPOSE: The integration of pharmacies with oncology practices-known as medically integrated dispensing or in-office dispensing-could improve care coordination but may incentivize overprescribing or inappropriate prescribing. Because little is known about this emerging phenomenon, we analyzed historical trends in medically integrated dispensing. METHODS: Annual IQVIA data on oncologists were linked to 2010-2019 National Council for Prescription Drug Programs pharmacy data; data on commercially insured patients diagnosed with any of six common cancer types; and summary data on providers' Medicare billing. We calculated the national prevalence of medically integrated dispensing among community and hospital-based oncologists. We also analyzed the characteristics of the oncologists and patients affected by this care model. RESULTS: Between 2010 and 2019, the percentage of oncologists in practices with medically integrated dispensing increased from 12.8% to 32.1%. The share of community oncologists in dispensing practices increased from 7.6% to 28.3%, whereas the share of hospital-based oncologists in dispensing practices increased from 18.3% to 33.4%. Rates of medically integrated dispensing varied considerably across states. Oncologists who dispensed had higher patient volumes (P < .001) and a smaller share of Medicare beneficiaries (P < .001) than physicians who did not dispense. Patients treated by dispensing oncologists had higher risk and comorbidity scores (P < .001) and lived in areas with a higher % Black population (P < .001) than patients treated by nondispensing oncologists. CONCLUSION: Medically integrated dispensing has increased significantly among oncology practices over the past 10 years. The reach, clinical impact, and economic implications of medically integrated dispensing should be evaluated on an ongoing basis.

Performance Drift in a Mortality Prediction Algorithm during the SARS-CoV-2 Pandemic.

Research Objective: Health systems use clinical predictive algorithms to allocate resources to high-risk patients. Such algorithms are trained using historical data and are later implemented in clinical settings. During this implementation period, predictive algorithms are prone to performance changes ("drift") due to exogenous shocks in utilization or shifts in patient characteristics. Our objective was to examine the impact of sudden utilization shifts during the SARS-CoV-2 pandemic on the performance of an electronic health record (EHR)-based prognostic algorithm. Study Design: We studied changes in the performance of Conversation Connect, a validated machine learning algorithm that predicts 180-day mortality among outpatients with cancer receiving care at medical oncology practices within a large academic cancer center. Conversation Connect generates mortality risk predictions before each encounter using data from 159 EHR variables collected in the six months before the encounter. Since January 2019, Conversation Connect has been used as part of a behavioral intervention to prompt clinicians to consider early advance care planning conversations among patients with ≥10% mortality risk. First, we descriptively compared encounter-level characteristics in the following periods: January 2019-February 2020 ("pre-pandemic"), March-May 2020 ("early-pandemic"), and June-December 2020 ("later-pandemic"). Second, we quantified changes in high-risk patient encounters using interrupted time series analyses that controlled for pre-pandemic trends and demographic, clinical, and practice covariates. Our primary metric of performance drift was false negative rate (FNR). Third, we assessed contributors to performance drift by comparing distributions of key EHR inputs across periods and predicting later pandemic utilization using pre-pandemic inputs. Population Studied: 237,336 in-person and telemedicine medical oncology encounters. Principal Findings: Age, race, average patient encounters per month, insurance type, comorbidity counts, laboratory values, and overall mortality were similar among encounters in the pre-, early-, and later-pandemic periods. Relative to the pre-pandemic period, the later-pandemic period was characterized by a 6.5-percentage-point decrease (28.2% vs. 34.7%) in high-risk encounters (p<0.001). FNR increased from 41.0% (95% CI 38.0-44.1%) in the pre-pandemic period to 57.5% (95% CI 51.9-63.0%) in the later pandemic period. Compared to the pre-pandemic period, the early and later pandemic periods had higher proportions of telemedicine encounters (0.01% pre-pandemic vs. 20.0% early-pandemic vs. 26.4% later-pandemic) and encounters with no preceding laboratory draws (17.7% pre-pandemic vs. 19.8% early-pandemic vs. 24.1% later-pandemic). In the later pandemic period, observed laboratory utilization was lower than predicted (76.0% vs 81.2%, p<0.001). In the later-pandemic period, mean 180-day mortality risk scores were lower for telemedicine encounters vs. in-person encounters (10.3% vs 11.2%, p<0.001) and encounters with no vs. any preceding laboratory draws (1.5% vs. 14.0%, p<0.001). Conclusions: During the SARS-CoV-2 pandemic period, the performance of a machine learning prognostic algorithm used to prompt advance care planning declined substantially. Increases in telemedicine and declines in laboratory utilization contributed to lower performance. Implications for Policy or Practice: This is the first study to show algorithm performance drift due to SARS-CoV-2 pandemic-related shifts in telemedicine and laboratory utilization. These mechanisms of performance drift could apply to other EHR clinical predictive algorithms. Pandemic-related decreases in care utilization may negatively impact the performance of clinical predictive algorithms and warrant assessment and possible retraining of such algorithms.

Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial.

PURPOSE: Serious Illness Conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Although behavioral interventions may prompt earlier or more frequent SICs, their impact on the quality of SICs is unclear. METHODS: This was a secondary analysis of a randomized clinical trial (NCT03984773) among 78 clinicians and 14,607 patients with cancer testing the impact of an automated mortality prediction with behavioral nudges to clinicians to prompt more SICs. We analyzed 318 randomly selected SICs matched 1:1 by clinicians (159 control and 159 intervention) to compare the quality of intervention vs. control conversations using a validated codebook. Comprehensiveness of SIC documentation was used as a measure of quality, with higher integer numbers of documented conversation domains corresponding to higher quality conversations. A conversation was classified as high-quality if its score was ≥ 8 of a maximum of 10. Using a noninferiority design, mixed effects regression models with clinician-level random effects were used to assess SIC quality in intervention vs. control groups, concluding noninferiority if the adjusted odds ratio (aOR) was not significantly < 0.9. RESULTS: Baseline characteristics of the control and intervention groups were similar. Intervention SICs were noninferior to control conversations (aOR 0.99; 95% CI, 0.91 to 1.09). The intervention increased the likelihood of addressing patient-clinician relationship (aOR = 1.99; 95% CI, 1.23 to 3.27; P < .01) and decreased the likelihood of addressing family involvement (aOR = 0.56; 95% CI, 0.34 to 0.90; P < .05). CONCLUSION: A behavioral intervention that increased SIC frequency did not decrease their quality. Behavioral prompts may increase SIC frequency without sacrificing quality.

Automated Text-Based Symptom Monitoring With Rapid Clinician Triage for Patients With Cancer and Suspected or Confirmed COVID-19.

PURPOSE: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19. METHODS: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP. RESULTS: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4). CONCLUSION: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics.

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology.

PURPOSE: Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. The objective of this work is to develop a new modeling approach to predicting short-term mortality that accounts for heterogeneity across multiple subgroups in the presence of a large number of electronic health record predictors. METHODS: We proposed a two-stage approach to addressing heterogeneity among oncology patients of different cancer types for predicting their risk of mortality. Structured data were extracted from the University of Pennsylvania Health System for 20,723 patients of 11 cancer types, where 1,340 (6.5%) patients were deceased. We first modeled the overall risk for all patients without differentiating cancer types, as is done in the current practice. We then developed cancer type-specific models using the overall risk score as a predictor along with preselected type-specific predictors. The overall and type-specific models were compared with respect to discrimination using the area under the precision-recall curve (AUPRC) and calibration using the calibration slope. We also proposed metrics that characterize the degree of risk heterogeneity by comparing risk predictors in the overall and type-specific models. RESULTS: The two-stage modeling resulted in improved calibration and discrimination across all 11 cancer types. The improvement in AUPRC was significant for hematologic malignancies including leukemia, lymphoma, and myeloma. For instance, the AUPRC increased from 0.358 to 0.519 (∆ = 0.161; 95% CI, 0.102 to 0.224) and from 0.299 to 0.354 (∆ = 0.055; 95% CI, 0.009 to 0.107) for leukemia and lymphoma, respectively. For all 11 cancer types, the two-stage approach generated well-calibrated risks. A high degree of heterogeneity between type-specific and overall risk predictors was observed for most cancer types. CONCLUSION: Our two-stage modeling approach that accounts for cancer type-specific risk heterogeneity has improved calibration and discrimination than a model agnostic to cancer types.

Characteristics of Physicians Participating in Medicare's Oncology Care Model Bundled Payment Program.

PURPOSE: The Oncology Care Model (OCM) is Medicare's first bundled payment program for patients with cancer. We examined baseline characteristics of OCM physician participants and markets with high OCM physician participation to inform generalizability and complement the ongoing practice-level evaluation of the OCM. METHODS: In this cross-sectional study, we identified characteristics of US medical oncologists practicing in 2016, using a national telephone-verified physician database. We linked these data with Dartmouth Atlas and Medicare claims data from 2011 through 2016 to identify characteristics of markets with high OCM participation. We used logistic regression to examine relationships between market characteristics and OCM participation. RESULTS: Of 10,428 US medical oncologists, 2,605 (24.9%) were listed in an OCM practice. There were no differences in sex or medical training between OCM participants and nonparticipants, although OCM participants were slightly younger. OCM participants practiced in larger (median daily patient volume, 80 v 55 patients) and urban practices (95.2% v 90.7%) and were less likely to be part of a health system (41.0% v 60.4%) or solo practice (45.5% v 67.4%; all P < .001). Participation was higher in southern and mid-Atlantic markets. Markets with high OCM physician participation had higher specialist density, hospital care intensity, and acute care use at the end of life (all P < .001). Market-level penetration of Accountable Care Organizations (adjusted odds ratio, 4.65; 95% CI 3.31 to 6.56; P < .001) and Medicare Advantage (adjusted odds ratio 2.82; 95% CI, 1.97 to 4.06; P < .001) were associated with higher OCM participation. CONCLUSION: In the first description of oncologists participating in the OCM, we found differences in practice demographics, care intensity, and exposure to nontraditional payment models between OCM-participating and nonparticipating physicians. Such provider-level differences may not be captured in Medicare's practice-level analysis.

Using Big Data and Predictive Analytics to Determine Patient Risk in Oncology.

Big data and predictive analytics have immense potential to improve risk stratification, particularly in data-rich fields like oncology. This article reviews the literature published on use cases and challenges in applying predictive analytics to improve risk stratification in oncology. We characterized evidence-based use cases of predictive analytics in oncology into three distinct fields: (1) population health management, (2) radiomics, and (3) pathology. We then highlight promising future use cases of predictive analytics in clinical decision support and genomic risk stratification. We conclude by describing challenges in the future applications of big data in oncology, namely (1) difficulties in acquisition of comprehensive data and endpoints, (2) the lack of prospective validation of predictive tools, and (3) the risk of automating bias in observational datasets. If such challenges can be overcome, computational techniques for clinical risk stratification will in short order improve clinical risk stratification for patients with cancer.

Development of physiological resistance and its stage specificity in Culex quinquefasciatus after selection with deltamethrin in Assam, India.

The study investigated the development and stage specificity of physiological resistance to insecticides in a colony of Culex quinquefasciatus Say (Diptera: Culicidae) mosquitoes, which are vectors of bancroftian filariasis in India, after selection with deltamethrin. Resistance was selected by exposing the larvae to the concentration of deltamethrin that caused 50% mortality in the tested population (i.e., LC50). Under continuous selection pressure, the LC50 increased steadily in subsequent generations. The estimated LC50 for the F0 generation was 0.409 microg/L; the LC50 first displayed a substantial increase in the F5 generation (5.616 microg/L) and reached 121.902 microg/L in the F10 generation. The objective of this study was to establish a deltamethrin-resistant colony to develop a research programme that will study the evolution of physiological resistance patterns and stage-specific resistance responses in Cx. quinquefasciatus larvae and adults under laboratory conditions. An approximately 298-fold increase in resistance was recorded after 10 generations, as evidenced by the resistance ratio (RR50). The progress and effect of the selection pressure in the adult stage was monitored with the World Health Organisation (WHO) diagnostic test. The mortality, as observed using the WHO diagnostic test, declined significantly from the F5 generation (85%) onwards and the highest rate of survival (65%) was observed in the F10 generation.

Development of physiological resistance and its stage specificity in Culex quinquefasciatus after selection with deltamethrin in Assam, India

The study investigated the development and stage specificity of physiological resistance to insecticides in a colony of Culex quinquefasciatus Say (Diptera: Culicidae) mosquitoes, which are vectors of bancroftian filariasis in India, after selection with deltamethrin. Resistance was selected by exposing the larvae to the concentration of deltamethrin that caused 50 percent mortality in the tested population (i.e., LC50). Under continuous selection pressure, the LC50 increased steadily in subsequent generations. The estimated LC50 for the F0 generation was 0.409 μg/L; the LC50 first displayed a substantial increase in the F5 generation (5.616 μg/L) and reached 121.902 μg/L in the F10 generation. The objective of this study was to establish a deltamethrin-resistant colony to develop a research programme that will study the evolution of physiological resistance patterns and stage-specific resistance responses in Cx. quinquefasciatus larvae and adults under laboratory conditions. An approximately 298-fold increase in resistance was recorded after 10 generations, as evidenced by the resistance ratio (RR50). The progress and effect of the selection pressure in the adult stage was monitored with the World Health Organisation (WHO) diagnostic test. The mortality, as observed using the WHO diagnostic test, declined significantly from the F5 generation (85 percent) onwards and the highest rate of survival (65 percent) was observed in the F10 generation.