Hemodiafiltration: Technical and Medical Insights.

Despite the significant medical and technical improvements in the field of dialytic renal replacement modalities, morbidity and mortality are excessively high among patients with end-stage kidney disease, and most interventional studies yielded disappointing results. Hemodiafiltration, a dialysis method that was implemented in clinics many years ago and that combines the two main principles of hemodialysis and hemofiltration-diffusion and convection-has had a positive impact on mortality rates, especially when delivered in a high-volume mode as a surrogate for a high convective dose. The achievement of high substitution volumes during dialysis treatments does not only depend on patient characteristics but also on the dialyzer (membrane) and the adequately equipped hemodiafiltration machine. The present review article summarizes the technical aspects of online hemodiafiltration and discusses present and ongoing clinical studies with regards to hard clinical and patient-reported outcomes.

Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial.

Importance: Serious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC. Objective: To test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm. Design, Setting, and Participants: This prespecified 40-week analysis of a stepped-wedge randomized clinical trial conducted between June 17, 2019, and April 20, 2020 (including 16 weeks of intervention rollout and 24 weeks of follow-up), included 20 506 patients with cancer representing 41 021 encounters at 9 tertiary or community-based medical oncology clinics in a large academic health system. The current analyses were conducted from June 1, 2021, to May 31, 2022. Intervention: High-risk patients were identified using a validated electronic health record machine learning algorithm to predict 6-month mortality. The intervention consisted of (1) weekly emails to clinicians comparing their SIC rates for all patients against peers' rates, (2) weekly lists of high-risk patients, and (3) opt-out text messages to prompt SICs before encounters with high-risk patients. Main Outcomes and Measures: The primary outcome was SIC rates for all and high-risk patient encounters; secondary end-of-life outcomes among decedents included inpatient death, hospice enrollment and length of stay, and intensive care unit admission and systemic therapy close to death. Intention-to-treat analyses were adjusted for clinic and wedge fixed effects and clustered at the oncologist level. Results: The study included 20 506 patients (mean [SD] age, 60.0 [14.0] years) and 41 021 patient encounters: 22 259 (54%) encounters with female patients, 28 907 (70.5%) with non-Hispanic White patients, and 5520 (13.5%) with high-risk patients; 1417 patients (6.9%) died by the end of follow-up. There were no meaningful differences in demographic characteristics in the control and intervention periods. Among high-risk patient encounters, the unadjusted SIC rates were 3.4% (59 of 1754 encounters) in the control period and 13.5% (510 of 3765 encounters) in the intervention period. In adjusted analyses, the intervention was associated with increased SICs for all patients (adjusted odds ratio, 2.09 [95% CI, 1.53-2.87]; P < .001) and decreased end-of-life systemic therapy (7.5% [72 of 957 patients] vs 10.4% [24 of 231 patients]; adjusted odds ratio, 0.25 [95% CI, 0.11-0.57]; P = .001) relative to controls, but there was no effect on hospice enrollment or length of stay, inpatient death, or end-of-life ICU use. Conclusions and Relevance: In this randomized clinical trial, a machine learning-based behavioral intervention and behavioral nudges to clinicans led to an increase in SICs and reduction in end-of-life systemic therapy but no changes in other end-of-life outcomes among outpatients with cancer. These results suggest that machine learning and behavioral nudges can lead to long-lasting improvements in cancer care delivery. Trial Registration: ClinicalTrials.gov Identifier: NCT03984773.

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial.

BACKGROUND: While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer. METHODS AND FINDINGS: Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention. CONCLUSIONS: Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients.

Integrating Monitoring of Volume Status and Blood Volume-Controlled Ultrafiltration into Extracorporeal Kidney Replacement Therapy.

PURPOSE: Volume management in hemodialysis (HD) requires the ability to assess volume status objectively and determine treatment strategies that achieve euvolemia without compromising hemodynamic stability. The aim of this study was to compare dialysis with and without blood volume-controlled ultrafiltration (UF) in combination with body composition monitoring, and to evaluate indicators for adequate dialysis (Kt/V), ultrafiltration volume, fluid status, and the occurrence of intradialytic morbid events (IME). PATIENTS AND METHODS: Patients undergoing hemodialysis or on-line hemodiafiltration with support of a blood volume monitor (BVM) - a feedback control device integrated into the 5008 and 6008 HD systems - were enrolled. Patients received treatment for four weeks using the 6008 CAREsystem and the BVM (6008+). Data on dialysis dose (Kt/V), UF volume and predialysis fluid status were documented. This data was also documented retrospectively for four weeks with (5008+) and without (5008-) the use of the BVM with the 5008 system. Comparisons were analyzed using linear mixed models. RESULTS: Twenty-four patients were enrolled. Kt/V was unaffected by blood volume-controlled UF (5008- vs 5008+: p=0.230) and was equally achieved with both HD systems (5008+ vs 6008+: p=0.922). The UF volume and fluid status achieved were comparable, independent of the use of UF control with BVM (5008- vs 5008+; UF volume: p=0.166; fluid overload: p=0.390) or the HD system (5008+ vs 6008+: UF volume: p=0.003; fluid overload: p=0.838), except for UF volume being higher in the 6008+ phase. IMEs occurred in less than 3% of treatments, with no difference between study phases. CONCLUSION: This study demonstrates that a clinical approach to kidney replacement therapy that tracks volume status and manages intradialytic fluid removal by blood volume-controlled UF delivers adequate dialysis without compromising fluid removal. It maintains volume status and ensures low incidence of IMEs.

Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients With Cancer.

IMPORTANCE: Machine learning (ML) algorithms can identify patients with cancer at risk of short-term mortality to inform treatment and advance care planning. However, no ML mortality risk prediction algorithm has been prospectively validated in oncology or compared with routinely used prognostic indices. OBJECTIVE: To validate an electronic health record-embedded ML algorithm that generated real-time predictions of 180-day mortality risk in a general oncology cohort. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study comprised a prospective cohort of patients with outpatient oncology encounters between March 1, 2019, and April 30, 2019. An ML algorithm, trained on retrospective data from a subset of practices, predicted 180-day mortality risk between 4 and 8 days before a patient's encounter. Patient encounters took place in 18 medical or gynecologic oncology practices, including 1 tertiary practice and 17 general oncology practices, within a large US academic health care system. Patients aged 18 years or older with outpatient oncology or hematology and oncology encounters were included in the analysis. Patients were excluded if their appointment was scheduled after weekly predictions were generated and if they were only evaluated in benign hematology, palliative care, or rehabilitation practices. EXPOSURES: Gradient-boosting ML binary classifier. MAIN OUTCOMES AND MEASURES: The primary outcome was the patients' 180-day mortality from the index encounter. The primary performance metric was the area under the receiver operating characteristic curve (AUC). RESULTS: Among 24 582 patients, 1022 (4.2%) died within 180 days of their index encounter. Their median (interquartile range) age was 64.6 (53.6-73.2) years, 15 319 (62.3%) were women, 18 015 (76.0%) were White, and 10 658 (43.4%) were seen in the tertiary practice. The AUC was 0.89 (95% CI, 0.88-0.90) for the full cohort. The AUC varied across disease-specific groups within the tertiary practice (AUC ranging from 0.74 to 0.96) but was similar between the tertiary and general oncology practices. At a prespecified 40% mortality risk threshold used to differentiate high- vs low-risk patients, observed 180-day mortality was 45.2% (95% CI, 41.3%-49.1%) in the high-risk group vs 3.1% (95% CI, 2.9%-3.3%) in the low-risk group. Integrating the algorithm into the Eastern Cooperative Oncology Group and Elixhauser comorbidity index-based classifiers resulted in favorable reclassification (net reclassification index, 0.09 [95% CI, 0.04-0.14] and 0.23 [95% CI, 0.20-0.27], respectively). CONCLUSIONS AND RELEVANCE: In this prognostic study, an ML algorithm was feasibly integrated into the electronic health record to generate real-time, accurate predictions of short-term mortality for patients with cancer and outperformed routinely used prognostic indices. This algorithm may be used to inform behavioral interventions and prompt earlier conversations about goals of care and end-of-life preferences among patients with cancer.

Views From Patients With Cancer in the Setting of Unplanned Acute Care: Informing Approaches to Reduce Health Care Utilization.

PURPOSE: New oncology care delivery models that avoid preventable acute care are needed, yet it is unclear which interventions best meet the needs of patients and caregivers. Perspectives from patients who experienced unplanned acute care events may inform the successful development and implementation of care delivery models. METHODS: We performed a qualitative interview study of patients with solid tumors on active treatment who experienced the following 3 types of unplanned acute care events: emergency department visits, first hospitalizations, and multiple hospitalizations. Patients were prospectively recruited within a large academic health system from August 2018 to January 2019. Interviews followed a semi-structured guide developed from the Consolidated Framework for Implementation Research. The constant comparative approach was used to identify themes. RESULTS: Forty-nine patients were interviewed; 51% were men, 75% were non-Hispanic White, and the mean age was 57.4 years (standard deviation, 1.9 years). Fifty-five percent of patients had metastatic disease, and 33% had an Eastern Cooperative Oncology Group performance status of 3-4. We identified the following key themes: drivers of the decision to seek acute care, patients' emotional concerns that influence interactions with the oncology team, and strategies used to avoid acute care. Patients' recommendations for interventions included anticipatory guidance, peer support, improved triage methods, and enhanced symptom management. Patients preferred options for virtual and home-based outpatient care. CONCLUSION: Patient-centered care models should focus on early delivery of supportive interventions that help patients and caregivers navigate the unexpected issues that come with cancer treatment. Patients advocate for proactive, multidisciplinary supportive interventions that enable home-based care and are led by the primary oncology team.

Management of Lung Cancer During the COVID-19 Pandemic.

Coronavirus disease 2019 (COVID-19) has had a devastating impact around the world. With high rates of transmission and no curative therapies or vaccine yet available, the current cornerstone of management focuses on prevention by social distancing. This includes decreased health care contact for patients. Patients with lung cancer are a particularly vulnerable population, where the risk of mortality from cancer must now be balanced by the potential risk of a life-threatening infection. In these unprecedented times, a collaborative and multidisciplinary approach is required to streamline but not compromise care. We have developed guidelines at our academic cancer center to standardize management of patients with lung cancer across our health care system and provide guidance to the larger oncology community. We recommend that general principles of lung cancer treatment continue to be followed in most cases where delays could result in rapid cancer progression. We recognize that our recommendations may change over time based on clinical resources and the evolving nature of the COVID-19 pandemic. In principle, however, treatment paradigms must continue to be individualized, with careful consideration of risks and benefits of continuing or altering lung cancer-directed therapy.

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer.

Importance: Machine learning algorithms could identify patients with cancer who are at risk of short-term mortality. However, it is unclear how different machine learning algorithms compare and whether they could prompt clinicians to have timely conversations about treatment and end-of-life preferences. Objectives: To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer. Design, Setting, and Participants: Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019. Exposures: Logistic regression, gradient boosting, and random forest algorithms. Main Outcomes and Measures: Primary outcome was 180-day mortality from the index encounter; secondary outcome was 500-day mortality from the index encounter. Results: Among 26 525 patients in the analysis, 1065 (4.0%) died within 180 days of the index encounter. Among those who died, the mean age was 67.3 (95% CI, 66.5-68.0) years, and 500 (47.0%) were women. Among those who were alive at 180 days, the mean age was 61.3 (95% CI, 61.1-61.5) years, and 15 922 (62.5%) were women. The population was randomly partitioned into training (18 567 [70.0%]) and validation (7958 [30.0%]) cohorts at the patient level, and a randomly selected encounter was included in either the training or validation set. At a prespecified alert rate of 0.02, positive predictive values were higher for the random forest (51.3%) and gradient boosting (49.4%) algorithms compared with the logistic regression algorithm (44.7%). There was no significant difference in discrimination among the random forest (area under the receiver operating characteristic curve [AUC], 0.88; 95% CI, 0.86-0.89), gradient boosting (AUC, 0.87; 95% CI, 0.85-0.89), and logistic regression (AUC, 0.86; 95% CI, 0.84-0.88) models (P for comparison = .02). In the random forest model, observed 180-day mortality was 51.3% (95% CI, 43.6%-58.8%) in the high-risk group vs 3.4% (95% CI, 3.0%-3.8%) in the low-risk group; at 500 days, observed mortality was 64.4% (95% CI, 56.7%-71.4%) in the high-risk group and 7.6% (7.0%-8.2%) in the low-risk group. In a survey of 15 oncology clinicians with a 52.1% response rate, 100 of 171 patients (58.8%) who had been flagged as having high risk by the gradient boosting algorithm were deemed appropriate for a conversation about treatment and end-of-life preferences in the upcoming week. Conclusions and Relevance: In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.

Loss of stromal JUNB does not affect tumor growth and angiogenesis.

The transcription factor AP-1 subunit JUNB has been shown to play a pivotal role in angiogenesis. It positively controls angiogenesis by regulating Vegfa as well as the transcriptional regulator Cbfb and its target Mmp13. In line with these findings, it has been demonstrated that tumor cell-derived JUNB promotes tumor growth and angiogenesis. In contrast to JUNB's function in tumor cells, the role of host-derived stromal JUNB has not been elucidated so far. Here, we show that ablation of Junb in stromal cells including endothelial cells (ECs), vascular smooth muscle cells (SMCs) and fibroblasts does not affect tumor growth in two different syngeneic mouse models, the B16-F1 melanoma and the Lewis lung carcinoma model. In-depth analyses of the tumors revealed that tumor angiogenesis remains unaffected as assessed by measurements of the microvascular density and relative blood volume in the tumor. Furthermore, we could show that the maturation status of the tumor vasculature, analyzed by the SMC marker expression, α-smooth muscle actin and Desmin, as well as the attachment of pericytes to the endothelium, is not changed upon ablation of Junb. Taken together, these results indicate that the pro-angiogenic functions of stromal JUNB are well compensated with regard to tumor angiogenesis and tumor growth.

Endothelial cell ephrinB2-dependent activation of monocytes in arteriosclerosis.

OBJECTIVE: The expression of ephrinB2 in endothelial cells delineates their arterial phenotype and is a prerequisite for the development of the embryonic vasculature. Whereas the role of ephrinB2 in the microcirculation has been studied extensively, its expression and function in adult arteries is hardly understood. METHODS AND RESULTS: Our analyses showed that in mouse arteries, ephrinB2 is located on the luminal surface of endothelial cells and may physically interact with monocyte EphB receptors. Moreover, transdifferentiation of human monocytes into macrophages was associated with an increase in EphB2 expression, and exposing monocytes to immobilized ephrinB2 resulted in phosphorylation of the receptor followed by an increased expression of proinflammatory chemokines such as interleukin-8 and monocyte chemotactic protein-1/CCL2. Relating to the (patho)physiological relevance of these findings, immunofluorescence analyses revealed that ephrinB2 is most abundantly expressed in endothelial cells at arteriosclerosis predilection sites of the mouse aorta. Subsequent analyses indicated that monocyte adhesion to aortic segments abundantly expressing ephrinB2 is strongly enhanced and that endothelial cell ephrinB2 forward signaling is sufficient to upregulate cytokine expression in monocytes. CONCLUSIONS: These observations suggest a hitherto unknown link between vascular ephrinB2 expression and the proinflammatory activation of monocytes that may contribute to the pathogenesis of arteriosclerosis.

Role of ephrinB2 expression in endothelial cells during arteriogenesis: impact on smooth muscle cell migration and monocyte recruitment.

Expression of the arterial marker molecule ephrinB2 in endothelial cells is a prerequisite for adequate remodeling processes of the developing or angiogenic vasculature. Although its role in these processes has been extensively studied, the impact of ephrinB2 on the remodeling of adult arteries is largely unknown. To this end, we analyzed its expression during a biomechanically induced arteriolar remodeling process known as arteriogenesis and noted a significant increase in ephrinB2 expression under these conditions. By examining those biomechanical forces presumed to drive arteriogenesis, we identified cyclic stretch as a critical inducer of ephrinB2 expression in endothelial cells. Subsequent functional analyses in vitro revealed that endothelial cells expressing ephrinB2 limit the migration of smooth muscle cells, thereby enhancing segregation of both cell types. Moreover, MCP-1 induced transmigration of monocytes through a monolayer of endothelial cells overexpressing a truncated variant of ephrinB2 was clearly impeded. Taken together, these data suggest that expression of ephrinB2 in adult endothelial cells is up-regulated during arterial remodeling and controlled by cyclic stretch, a well-known inducer of such processes. This stretch-induced ephrinB2 expression may be pivotal for arteriogenesis as it limits smooth muscle cell migration within defined borders and controls monocyte extravasation.

Diffusion and saponification inside porous cellulose triacetate fibers.

Cellulose triacetate (CTA) fibers were partially hydrolyzed in 0.054 N solutions of NaOH/H(2)O and NaOMe/MeOH. The surface concentration of acetyl groups was determined using ATR-FTIR. Total acetyl content was determined by the alkaline hydrolysis method. Fiber cross-sections were stained with Congo red in order to examine the interface between reacted and unreacted material; these data were used to estimate the rate constant k and effective diffusivity D(B) for each reagent during the early stages of reaction by means of a volume-based unreacted core model. For NaOH/H(2)O, k = 0.37 L mol(-1) min(-1) and D(B) = 6.2 x 10(-7) cm(2)/sec; for NaOMe/MeOH, k = 4.0 L mol(-1) min(-1) and D(B) = 5.7 x 10(-6) cm(2)/sec. The NaOMe/MeOH reaction has a larger rate constant due to solvent effects and the greater nucleophilicity of MeO(-) as compared to OH(-); the reaction has a larger effective diffusivity because CTA swells more in MeOH than it does in water. Similarities between calculated concentration profiles for each case indicate that the relatively diffuse interface seen in fibers from the NaOMe/MeOH reaction results from factors not considered in the model; shrinkage of stained fiber cross-sections suggests that increased disruption of intermolecular forces may be the cause.

Postdelivery of alfentanil and ketamine has no effect on intradermal capsaicin-induced pain and hyperalgesia.

OBJECTIVE: The predelivery of intravenous alfentanil (a mu opioid agonist) and ketamine (an -methyl d-aspartate antagonist) has recently been shown to decrease the secondary hyperalgesia induced by intradermal capsaicin. The focus of this study was to determine the effects of the postdelivery of intravenous alfentanil and ketamine on intradermal capsaicin-induced secondary hyperalgesia. DESIGN: Double-blind, placebo-controlled, randomized, crossover study. Five minutes after an intradermal capsaicin injection, alfentanil and ketamine infusions were administered for a target plasma concentration of 75 ng/ml for alfentanil and 150 ng/ml for ketamine or placebo equivalent using a computer-controlled infusion pump and maintained for the remainder of the study. The investigator recorded the magnitude of the pain score at the time of injection and at 5-minute intervals. Fifteen minutes after the intradermal capsaicin injection, the region of secondary hyperalgesia and flare response was determined. RESULTS: Alfentanil and ketamine plasma levels targeted after injection of intradermal capsaicin had no significant effect on pain scores, flare response, or secondary hyperalgesia. CONCLUSIONS: Consistent with animal studies on preemptive analgesia, this study demonstrates that alfentanil and ketamine have a differential effect when delivered before and after a painful stimulus. Because of the differential effect seen, future studies on the pharmacology of human experimental pain should evaluate both predrug and postdrug delivery.