Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting

PURPOSE

For patients with advanced cancer, timely referral to palliative care (PC) services can ensure that end-of-life care aligns with their preferences and goals. Overestimation of life expectancy may result in underutilization of PC services, counterproductive treatment measures, and reduced quality of life for patients. We assessed the impact of a commercially available augmented intelligence (AI) tool to predict 30-day mortality risk on PC service utilization in a real-world setting.

METHODS

Patients within a large hematology-oncology practice were scored weekly between June 2018 and October 2019 with an AI tool to generate insights into short-term mortality risk. Patients identified by the tool as being at high or medium risk were assessed for a supportive care visit and further referred as appropriate. Average monthly rates of PC and hospice referrals were calculated 5 months predeployment and 17 months postdeployment of the tool in the practice.

RESULTS

The mean rate of PC consults increased from 17.3 to 29.1 per 1,000 patients per month (PPM) pre- and postdeployment, whereas the mean rate of hospice referrals increased from 0.2 to 1.6 per 1,000 PPM. Eliminating the first 6 months following deployment to account for user learning curve, the mean rate of PC consults nearly doubled over baseline to 33.0 and hospice referrals increased 12-fold to 2.4 PPM.

CONCLUSION

Deployment of an AI tool at a hematology-oncology practice was found to be feasible for identifying patients at high or medium risk for short-term mortality. Insights generated by the tool drove clinical practice changes, resulting in significant increases in PC and hospice referrals.

Physician satisfaction with electronic medical records (EMRs): Time for an intelligent health record?

318

Background: EMRs are devised to improve the quality and efficiency of healthcare delivery and to reduce medical errors. Despite the widespread use of EMRs, various factors can limit their effectiveness in improving healthcare quality. General EMR use has been cited as a factor contributing to increased workload and clinician burnout in oncology and other specialties. The objective of this qualitative research study was to identify barriers perceived by medical oncologists and hematologists (mO/H) in utilizing EMR software and factors associated with levels of satisfaction. Methods: Between January and April 2021, mO/H from across the U.S. were invited to complete a web-based survey about various trends and critical issues in oncology care. Demographics about the physicians and characteristics of their practices were captured as well in the survey. Responses were aggregated and analyzed using descriptive statistics. Results: A total of 369 mO/H completed the survey: 72% practice in a community setting; 47% identified as a hospital employee; they have an average of 19 years of clinical experience and spend on average 86% of their working time in direct patient care, seeing 17 patients per day on average on clinic days. Most (99%) of mO/H surveyed use an EMR software at their practice, with Epic (45%) and OncoEMR (16%) being the most common. Regarding satisfaction, 16% and 50% reported feeling highly satisfied and satisfied, respectively, with their current EMR, and 3% and 11% reported feeling very dissatisfied or dissatisfied, respectively. Some (19%) stated that they have considered changing their EMR, and 68% are unsure how EMR licensing fees for their practice are paid. EMR pain points most commonly experienced were: time-consuming, e.g., too many steps/click (70%); interoperability, e.g., difficulty sharing information across institutions or other EMR software (45%); data entry issues, e.g., difficulty entering clinical information, scheduling patient visits and reminders, or ordering multiple labs (38%); and poor workflow support (31%). The most useful aspects/features of their EMR software reported were availability of information, e.g., preloaded protocols, chemotherapy regimens and pathways (64%); data access (64%); and multiple access points, including remote access (37%). Conclusions: Satisfaction with EMR were generally positive among the mO/H surveyed. However, there are multiple deterrents to the efficient use of current EMR systems. This information is essential in the design of next-generation EMR (an Intelligent Medical Records system) to allow for incorporation of aspects most useful to the end-users, such as pathway access, preloaded information on cancer management as well as ease of access and portability, and a user experience that minimizes clicks and reduces physician time with EMR.

Barriers to participation and success in value-based care (VBC) models

70

Background: The enactment of the Medicare Access and CHIP Reauthorization Act (MACRA) in 2015 initiated the transition from a fee-for-service to a fee-for-value payment system in healthcare. Two government-sponsored VBC models (Oncology Care Model [OCM] and Merit-Based Incentive Payment System [MIPS]) were introduced in 2016 and 2017. Several commercial payers followed suit with similar value-based contracts. Implementing and complying with the performance metrics of these models comes with challenges. This qualitative research study sought to assess participation in both government-sponsored and commercial-insurance-sponsored VBC models among oncology providers and their perceptions regarding the ability to perform successfully. Methods: Between February and April 2021, medical oncologists/hematologists (mO/H) from across the U.S. were invited to complete a web-based survey about various trends and critical issues in oncology care. Demographics about the physicians and characteristics of their practices were captured as well in the survey. Responses were aggregated and analyzed using descriptive statistics. Results: A total of 307 mO/H across the U.S. completed the survey: 73% practice in a community setting; 47% identify as hospital employees. The participants spend a median of 90% of their working time in direct patient care, have a median of 16 years of clinical experience, and see a median of 20 patients per day on clinic days. Half participate in a government-sponsored VBC model (21% MIPS and 28% OCM), and 20% participate in a commercial VBC model. A third reported that it is difficult to perform favorably in VBC models (37% government and 35% commercial). Primary challenges deterring favorable performance were navigating the payer landscape and reimbursements (27%), identifying cost-reduction opportunities (20%), tracking costs across an episode (18%), and clinical decision support and compliance (17%). One-third are not satisfied with currently available technology to effectively support their performance in VBC models. Almost half would like to see more seamless integration into practice workflows (49%) and interoperability across platforms including EHRs (42%), and 24% would like artificial intelligence or machine learning features integrated into solutions tools. Conclusions: Oncology practices find it challenging to perform favorably in government and private payer-sponsored VBC models. They are generally dissatisfied with current technology and see an unmet need for interoperability and artificial intelligence to better support their performance in these programs. Further research is needed to determine how best to design and implement VBC programs.

Augmented intelligence to predict 30-day mortality in patients with cancer

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients' electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.

Outcomes among patients with cancer previously identified as being at risk for 30-day mortality using augmented intelligence

12031

Background: An augmented intelligence (AI) tool using a machine learning algorithm was developed and validated to generate insights into risk for short-term mortality among patients with cancer. The algorithm, which scores patients every week as being at low, medium or high risk for death within 30 days, allowing providers to potentially intervene and modify care of those at medium to high risk based on established practice pathways. Deployment of the algorithm increased palliative care referrals in a large community hematology/oncology practice in the United States (Gajra et al, JCO 2020). The objective of this retrospective analysis was to evaluate the differences in survival and healthcare utilization (HCU) outcomes of patients previously scored as medium or high risk by the AI tool. Methods: Between 6/2018 – 10/2019, the AI tool scored patients on a weekly basis at the hematology/oncology practice. In 9/2020, a chart review was conducted for the 886 patients who had been identified by the algorithm as being at medium or high risk for 30-day mortality during the index period, to determine outcomes (including death, emergency department [ED] visits, and hospital admissions). Data are presented using descriptive statistics. Results: Of the 886 at-risk patients, 450 (50.8%) were deceased at the time of follow-up. Of these, 244 (54.2%) died within the first 180 days of scoring as at-risk, with median time to death 68 days (IQR 99). Among the 255 patients scored as high risk, 171 (67.1%) had died, vs. 279 (44.2%) of the 631 patients who were scored as medium risk (p < 0.001). Of the 601 patients who were scored more than once during the index period as medium or high risk, 342 (56.9%) had died, vs. 108 (37.9%) of the 285 who were scored as at risk only once (p < 0.001). A total of 363 patients (43.1%) had at least 1 ED visit, and 346 patients (41.1%) had at least 1 hospital admission. There was no difference in the proportion of patients scored as high risk compared with those scored as medium risk in ED visits (104 of 237 [43.9%] vs. 259 of 605 [42.8%], p = 0.778) or hospital admissions (100 of 237 [42.2%] vs. 246 of 605 [40.7%], p = 0.684, respectively). Compared with patients scored as medium or high risk only once during the index period, patients who were scored as at-risk more than once had more ED visits (282 of 593 [47.6%] vs. 81 of 249 [32.5%], p < 0.001) and hospital admissions (269 of 593 [45.4%] vs. 77 of 249 [30.9%], p < 0.001). Conclusions: This follow-up study found that half of the patients identified as at-risk for short-term mortality during the index period were deceased, with greater likelihood associated with high risk score and being scored more than once. Over 40% had visited an ED or were admitted to hospital. These findings have important implications for the use of the algorithm to guide treatment discussions, prevent acute HCU and to plan ahead for end of life care in patients with cancer.

Impact of augmented intelligence (AI) on identification and management of depression in oncology

e14059

Background: Depression is common in patients with cancer and is associated with worse cancer treatment outcomes. Depression is often underdiagnosed/treated as cancer clinicians are focused on the complex aspects of therapy and care coordination. AI has a potential application in the identification of patients at high risk for depression. Jvion has developed a prescriptive analytics solution (the Machine), which uses AI algorithms and machine learning techniques applied to combined clinical and exogenous datasets to identify patients with a propensity for poor clinical outcomes. The Machine was applied to depression risk (within next 6 months), and recommended patient-specific, dynamic, and actionable insights. While the Machine requires no additional documentation within the electronic health record (EHR) to generate its insights, those insights can be integrated back in to any EHR. Herein, we report the results of a pilot study evaluating the impact of AI-driven insights on depression screening and management at a single oncology practice. Methods: All patients were scored weekly using the Machine depression vector. The Machine risk-stratified the patients and generated recommendations for the provider to consider as they developed a care plan. Patients identified as “at risk” by the Machine were assessed for depression (PHQ-9) by the clinical team regardless of prior screening results. The rate per 1000 unique patients per month (PPM) of depression screenings, case management evaluations, and antidepressant prescriptions were calculated for the 5 months prior to and 17 months post deployment of the Machine in the practice. Results: The oncology practice has 21 providers managing an average of 4329 unique PPM. The mean rate of depression screenings increased from 6.0 per 1000 PPM pre- deployment to 16.2 per 1000 PPM post deployment (+271%). The downstream workflow outcomes of case management evaluations increased from 11.6 to 21.4 per 1000 PPM (+184%) and antidepressant prescriptions increased from 9.2 to 15.5 per 1000 PPM (+168%) pre and post-implementation respectively. The providers reported high satisfaction with the use of the AI solution in depression screening. Conclusions: This oncology practice found deployment of the Jvion AI solution to be feasible. The Machine-generated insights for depression risk were actionable, could be incorporated into workflow, and increased the number of patients identified. If confirmed in larger studies, AI-driven insights may improve the identification and management of depression in patients with cancer.

Impact of augmented intelligence (AI) on utilization of palliative care (PC) services in oncology

12015

Background: Timely integration of palliative care in the management of patients with advanced cancer is a quality benchmark in oncology. However, PC is often underutilized as evidenced by delays in identification of appropriate patients, in referrals to a PC service, and in enrollment to hospice. Jvion has developed a prescriptive analytics solution, the Machine, which combines AI algorithms with machine learning techniques and applies them to clinical and exogenous datasets to identify patients with a propensity for poor outcomes. The Machine was applied to risk for patients’ mortality within next 30 days, and recommended patient-specific, dynamic, and actionable insights. Use of the Machine requires no additional documentation within the electronic health record (EHR) and the insights generated can be integrated back in to any EHR to help inform the care plan. Herein, we report the results of a study evaluating the impact of AI-driven insights on PC utilization at a large community oncology practice. Methods: All patients were scored weekly using the Machine PC vector. The Machine risk stratified the patients and generated recommendations for the provider to consider as they developed a care plan. Patients identified as “at risk” by the Machine were assessed for a supportive care visit (PC referral) and then were referred as deemed clinically appropriate. The average monthly rates of PC consults and hospice referrals were calculated 5 months prior to and for 17 months after the launch of the Machine in the practice. Results: The oncology practice has 21 providers managing an average of 4329 unique patients per month (PPM). The mean rate of PC consults increased from 17.3 to 29.1 per 1000 PPM pre and post Machine deployment respectively (+168%). The mean monthly rate of hospice referrals increased by 8-fold from 0.2 to 1.6 per 1000 PPM pre and post deployment respectively. Eliminating the first 6 months of Machine deployment to account for user learning curve, the mean rates of monthly PC consults nearly doubled over baseline to 33.0, and hospice referrals rose 12-fold to 2.4 per 1000 patients in months 7-17 post Machine deployment. Conclusions: This oncology practice found deployment of this novel AI solution to be feasible and effective at generating actionable insights. These AI driven insights could be incorporated into workflow and improved the decision-making for whether and when a patient should be referred to PC and/or hospice services for end of life care. Further study is needed to confirm the value of AI for management of cancer patients at end of life.

Artificial intelligence (AI) to improve patient outcomes in community oncology practices

e18098

Background: Artificial Intelligence(AI) for predictive analytics has been studied extensively in diagnostic imaging and genetic testing. Cognitive analytics adds by suggesting interventions that optimize health outcomes using real-time data and machine learning. Herein, we report the results of a pilot study of the Jvion, Inc. Cognitive Clinical Success Machine (CCSM), an eigen vector-based deep learning AI technology. Methods: The CCSM uses electronic medical record (EMR) and publicly available socioeconomic/behavioral databases to create a n-dimensional space within which patients are mapped along vectors resulting in thousands of relevant clusters of clinically/behaviorally similar patients. These clusters have a mathematical propensity to respond to a clinical intervention which are updated dynamically with new data from the site. The CCSM generates recommendations for the provider to consider as they develop a care plan based on the patients’ cluster. We tested and trained the CCSM technology at 3 US oncology practices for the risk (low, intermediate, high) of 4 specific outcomes: 30 day severe pain, 30 day mortality, 6 month clinical deterioration (ECOG-PS), and 6 month diagnosis of major depressive disorder (MDD). We report the accuracy of the CCSM based on the testing and training data sets. Area under the curve (AUC) was calculated to show goodness of fit of classification models for each outcome. Results: In the training/testing data set there were 371,787 patients from the 3 sites: female = 61.3%; age ≤ 50 = 21.3%, 51-65 = 26.9%, > 65 = 51.9%; white/Caucasian = 43.4%, black/African American = 5.9%, unknown race = 43.4%. Cancer types were unknown/missing for 66.3% of patients and stage for 90.4% of patients. AUC range per vector: 30 day severe/recurrent pain = 0.85-0.90; 30-day mortality = 0.86-0.97; 6-month ECOG-PS decline of 1 point = 0.88-0.92; and 6-month diagnosis of MDD = 0.77-0.90. Conclusions: The high AUC indicates good separation between true positives/negatives (proper model specification for classifying the risk of each outcome) regardless of the degree of missing data for variables including cancer type and stage. Following testing, a 6 month pilot program was implemented (06/2018-11/2018). Final results of the pilot program are pending.

A review of dolasetron as management of nausea and vomiting in cancer patients

Objective. To systematically review the literature about the pharmacology, pharmacokinetics, efficacy, dosing, and adverse effects of dolasetron, and to define its role in the management of chemotherapy-and radiation-induced nausea and vomiting.

Data Synthesis. A MedLine search was conducted using 5-HT3-receptor antagonists, antiemetics, chemotherapy toxicity, dolasetron, emesis, nausea, and vomiting as search terms. Reference lists and bibliographies of pertinent articles were also identified and reviewed. Both preclinical and clinical literature were reviewed and analyzed.

Data Synthesis. Dolasetron is a serotonin type 3 (5-HT3)-receptor antagonist with potent antiemetic effects in the management of nausea and vomiting. Following administration, dolasetron is rapidly converted to hydrodolasetron, which is believed to be responsible for the drug's antiemetic activity. Results of multiple studies have demonstrated the efficacy of this agent in the prevention of chemotherapy-induced emesis, including that induced by cisplatin. As a single agent, dolasetron produces a complete response rate (RR) in 44% to 57% of patients treated with cisplatin (≥70 mg/m2) and in 59% to 80% of patients treated with moderately emetogenic chemotherapy, such as cyclophosphamide, methotrexate, and fluorouracil (CMF) therapy. When combined with dexamethasone, the RRs are increased. Dolasetron is well tolerated, with headache (24%) and diarrhea (12%) the most commonly reported adverse effects. The efficacy and safety of dolasetron are comparable to those observed with other 5-HT3-receptor antagonists. According to four recently published clinical practice guidelines for use of antiemetics, dolasetron is an appropriate first-line option for the prevention of nausea and vomiting due to moderately to highly emetogenic chemotherapy. Further clinical trials will determine the optimal dose and the role of this highly effective antiemetic agent for other purposes, such as treatment of delayed emesis and emesis resulting from radiation therapy and high-dose chemotherapy followed by bone marrow transplantation.

6-Mercaptopurine hepatotoxicity during acute lymphocytic leukemia maintenance therapy

Purpose. To describe a case of 6-mercaptopurine acute fulminate hepatotoxicity.

Case Summary. A 65-year-old male with adult acute lymphocytic leukemia was receiving mainte nance therapy with 6-mercaptopurine and methotrex ate when he presented with jaundice, nausea, diar rhea, dysuria, right upper quadrant pain, and dark urine. He presented with elevated bilirubin, aspartate aminotransferase, and alanine aminotransferase. The work-up was negative for other causes of liver dam age. His hospital course included antibiotic therapy, vitamin K, and filgrastim. He was discharged without long-term morbidity from the event and had a normal ization of liver enzymes as an outpatient. Methotrex ate maintenance therapy was continued successfully as an outpatient for > 1 year.

Conclusion. 6-Mercaptopurine is not commonly considered as an agent causing acute hepatotoxicity, but should not be ruled out as a causative agent in the setting of concurrent methotrexate therapy. An epi sode of acute hepatotoxicity did not preclude the continued, safe use of methotrexate in this case.

Review : Angiogenesis: A new target for antineoplastic therapy

Objective. To review the pathophysiologic rationale and therapeutic applications of inhibiting angiogenesis in solid tumor growth.

Data Sources. A MEDLINE search of articles published from 1985 to 1995 and a CancerLit search of articles published from 1988 to 1995, using the MESH heading "neovascularization" and text words "angiogenesis" and "antiangiogenesis." References listed in identified publications were reviewed for additional pertinent literature.

Study Selection. All human trials evaluating angiogenesis inhibitors in malignant disease and pre- clinical trials that illustrate potential mechanisms of action of such agents were included.

Data Synthesis. Angiogenesis, the formation of new blood vessels, is necessary for the development of significant solid tumor growth. Inhibition of angio genesis is a unique mechanism of antineoplastic ther apy that does not use traditional cytotoxic actions. Four investigational antiangiogenic agents are cur rently being evaluated in phase I and II trials. Poten tially beneficial applications of angiogenesis inhibitors include suppression of occult and premalignant le sions, symptomatic control of angiogenesis-depen dent malignancies, and combination therapy with traditional antineoplastic agents.

Conclusion. Inhibition of angiogenesis is a new pharmacologic strategy that may prove useful in controlling malignant growth. A number of agents with antiangiogenic activity have been developed, and further study of these drugs will define their role in antineoplastic therapy.

Review : Infertility after chemotherapy: A review of the risks and strategies for prevention

Purpose. Infertility as a late complication of cancer chemotherapy, focusing on specific drug-related ef fects, effects observed in the treatment of certain malignancies, and strategies for prevention is re viewed.

Data Sources. A MEDLINE search of articles from 1966 to present was conducted using the terms infertility, antineoplastics, amenorrhea, azospermia, oogenesis, and spermatogenesis. Additional refer ences were identified using review articles and phar macology textbooks.

Study Selection. All human studies reported in English language were included. Animal studies were included when human data were insufficient or un available.

Data Synthesis. Data on the infertility effects of individual antineoplastic agents are difficult to inter pret for several reasons, including small sample sizes, lack of prechemotherapy fertility assessment, inade quate long-term follow-up, and use of regimens in cluding multiple agents. In general, the incidence and severity of antifertility effects are dependent on the total dosage delivered, duration of therapy, and age at exposure. The alkylating agents have the most signif icant effects on fertility. Fertility outcomes have been reported for several different malignancies, especially in patients cured of Hodgkin's disease and testicular cancer. Information on specific antineoplastic agents and cancers are reviewed. Several methods have been employed to decrease gonadotoxic effects, but none have been effective.

Conclusions. Infertility is a common late com plication of cancer chemotherapy that is receiving increasing attention as the number of cancer survi vors increases. Health care professionals should be aware of infertility risks associated with antineoplastic agents and certain malignancies, and patients should be informed of these risks as treatment decisions are made.

Granisetron vs ondansetron for prevention of nausea and vomiting in hematopoietic stem cell transplant patients: results of a prospective, double-blind, randomized trial

The serotonin type-3 (5-HT3) antagonists represent a significant advance in the prevention of acute nausea and vomiting (N/V) from highly emetogenic chemotherapy. We sought to determine if any differences in efficacy or adverse effects exist between two such agents, ondansetron and granisetron, during conditioning therapy for hematopoietic stem cell transplantation (HSCT). Patients were randomized to receive either ondansetron 0.15 mg/kg intravenously every 8 h or granisetron 10 microg/kg intravenously daily. Additionally, all patients received scheduled dexamethasone and lorazepam. Prophylaxis was continued until 24 h after completion of chemotherapy. Nausea and distress were measured subjectively with visual analog scales and emetic episodes were quantified. Of the 110 randomized patients, 96 were evaluable for efficacy and safety. No significant differences in efficacy were observed between the ondansetron- and granisetron-treated patients, evaluated by comparing the degree of nausea and distress, number of emetic episodes and overall control of emesis. The adverse effects were also comparable and no patients were removed from study because of severe toxicities. This trial demonstrates that ondansetron and granisetron are equally effective at preventing acute N/V associated with conditioning therapy frequently used for HSCT. The agent of choice should be based on drug acquisition cost or preference.

Overview of cancer-related anemia: focus on the potential role of darbepoetin alfa

Cancer-associated anemia is common and has many causes, including the effects of the underlying disease and cancer treatment. The effect of anemia on patients with cancer was not appreciated fully until relatively recently. Several well-designed studies have demonstrated the relationship between anemia and fatigue, and the effect of fatigue on quality of life. These data have resulted in a greater awareness of anemia in cancer and have increased the use of recombinant human erythropoietin (r-HuEPO, epoetin alfa) therapy for the treatment of anemia. Recombinant HuEPO produces a hemoglobin response in 50-60% of patients with cancer; however, to obtain this response rate, frequent dosing is required. Darbepoetin alfa, a recently developed erythropoietic protein, has a longer half-life than that of r-HuEPO, enabling less frequent dosing, and has a greater in vivo activity. In studies of patients with cancer who develop anemia, darbepoetin alfa has proved to be well tolerated and effective, and its advantages make it a potential improved treatment option for anemia in these patients.

Overview of the management of AIDS-related Kaposi's sarcoma

OBJECTIVE

To review the epidemiology, pathogenesis, clinical presentation, diagnosis, and staging of Kaposi's sarcoma (KS), as well as the current role of local and systemic therapies in the management of AIDS-related KS (AIDS-KS).

DATA SOURCES AND STUDY SELECTION

MEDLINE and CANCERLIT searches of the English-language medical literature were conducted. Emphasis was placed on studies published since the onset of the AIDS epidemic in the early 1980s. A manual review of selected bibliographies was also completed.

DATA SYNTHESIS

AIDS-KS is a disease with a heterogeneous presentation that affects approximately 20% of patients with AIDS. Although the proportion of AIDS patients developing this disease during the course of their illness is declining, the actual number of AIDS-KS cases is increasing. The etiology of AIDS-KS is not clear, but a sexually transmitted cofactor has been implicated. Recent reports demonstrate that a herpes-like virus may be responsible for the development of KS in patients with and without AIDS. Furthermore, the cellular origin of KS has not been identified and questions remain about whether KS represents a true malignancy. The system used in staging patients with AIDS-KS has changed dramatically since initial therapeutic trials were conducted; this may account for observed differences in outcome among trials. The immunologic status of patients is now included as part of the staging system, since it has prognostic significance. Since specific therapy for AIDS-KS is not curative and does not prolong survival, it should be directed at improving patient cosmesis and palliation of disease-related symptoms. Local therapy, such as radiation, cryotherapy, and intralesional chemotherapy, is recommended for the management of limited disease. Systemic interferon alfa or chemotherapy is indicated for disseminated disease. Interferon alfa is useful in patients with predominantly mucocutaneous disease and is most effective in patients with good prognostic factors, such as absence of B symptoms, no history of opportunistic infections, and a CD4 count of more than 200 cells/mm3. Interferon alfa alone or in combination with zidovudine produces responses in approximately 30% of AIDS-KS patients with good prognostic factors. Single-agent or combination chemotherapy is indicated for rapidly progressive or advanced AIDS-KS. Commonly used agents include doxorubicin, daunorubicin, bleomycin, vincristine, and vinblastine. Responses can be expected in at least 50% of patients treated with single-agent or combination chemotherapy. However, many patients are unable to tolerate the toxicity associated with systemic AIDS-KS therapy. Future research will focus on therapies that target the underlying pathogenesis of this disease.

CONCLUSIONS

The optimal therapy for patients with AIDS-KS has not been determined. Treatment is appropriately directed at palliation of disease-related symptoms as no therapy has been unequivocally proven to impact survival. Local therapies should be used in the management of localized disease, while systemic therapy is appropriate for disseminated disease. Interferon alfa is useful in patients with primarily mucocutaneous disease or asymptomatic visceral involvement. Chemotherapy is indicated in patients who have rapidly progressive or advanced disease. Therapy must be individualized according to the patient's disease course and other patient-specific factors.

Granisetron: the second serotonin-receptor antagonist

OBJECTIVE

To review the pharmacology, pharmacokinetics, clinical efficacy, and adverse effects of granisetron, focusing on critical analysis of published clinical trials and comparison with other antiemetic agents, including ondansetron.

DATA SOURCES

MEDLINE (1966-1995) and CANCERLIT (1991-1995) searches of English-language literature using the terms "granisetron" and "granisetron (rn)" were performed.

STUDY SELECTION AND DATA EXTRACTION

All articles were considered for possible inclusion in this review. Abstracts of clinical trials were included only when they were judged to add critical information not otherwise available in the medical literature. For studies published more than once, the most recent publication was cited.

DATA SYNTHESIS

Nausea and vomiting are rated by patients as the most distressing chemotherapy-related adverse effects and may produce potentially life-threatening complications. The discovery of the role of serotonin in nausea and vomiting and the development of selective serotonin3-receptor (5-HT3) antagonists has significantly diminished the incidence and consequences of chemotherapy-related nausea and vomiting. Granisetron is the second 5-HT3-receptor antagonist to be marketed in the US. Granisetron has been compared with other antiemetic agents, including ondansetron, against highly and moderately emetogenic chemotherapy. The results of these trials have shown granisetron to be superior to conventional antiemetics and as effective as ondansetron in the prevention of chemotherapy-induced nausea and vomiting. The optimal dose of granisetron has yet to be determined. Formulary decisions should be based on a cost comparison among the 5-HT3-receptor antagonists at individual institutions.

CONCLUSIONS

Granisetron is a safe, effective antiemetic agent for the management of nausea and vomiting caused by cancer chemotherapy.

Antiemetic Therapy in the Outpatient Oncology Setting

Nausea and vomiting (N/V) are well-recognized and potentially serious complications of cancer chemotherapy that can significantly impact therapeutic outcomes and overall quality of life. As the management of cancer patients moves to the outpatient setting, therapeutic strategies for N/V control must be adapted accordingly. The purpose of this article is to provide an overview of the pathophysiology and basic principles of N/V management and the available antiemetic agents, with an emphasis on applications in outpatient oncology. Development of antiemetic guidelines promotes selection of appropriate antiemetics to maximize N/V control, while minimizing associated cost. Use of oral antiemetics when possible also significantly reduces the cost of N/V management, without compromising therapeutic efficacy. In addition to designing an appropriate treatment regimen, measures for the early evaluation of N/V outcomes must also be instituted. Pharmacists can have an important role in ensuring optimal control of N/V in cancer patients.