Measuring symptom burden in patients with cancer during a pandemic: the MD Anderson symptom inventory for COVID-19 (MDASI-COVID).

BACKGROUND: Symptom expression in SARS-CoV-2 infection (COVID-19) may affect patients already symptomatic with cancer. Patient-reported outcomes (PROs) can describe symptom burden during the acute and postacute stages of COVID-19 and support risk stratification for levels of care. At the start of the COVID-19 pandemic, our purpose was to rapidly develop, launch through an electronic patient portal, and provide initial validation for a PRO measure of COVID-19 symptom burden in patients with cancer. METHODS: We conducted a CDC/WHO web-based scan for COVID-19 symptoms and a relevance review of symptoms by an expert panel of clinicians treating cancer patients with COVID-19 to create a provisional MD Anderson Symptom Inventory for COVID-19 (MDASI-COVID). English-speaking adults with cancer who tested positive for COVID-19 participated in the psychometric testing phase. Patients completed longitudinal assessments of the MDASI-COVID and the EuroQOL 5 Dimensions 5 Levels (EQ-5D-5L) utility index and visual analog scale, which were presented through an electronic health record patient portal. To test the validity of the MDASI-COVID to distinguish between known groups of patients, we hypothesized that patients hospitalized, including having a hospitalization extended, for COVID-19 versus those not hospitalized would experience higher symptom burden. Correlation of mean symptom severity and interference scores with relevant EQ-5D-5L scores tested concurrent validity. The reliability of the MDASI-COVID was evaluated by calculating Cronbach alpha coefficients and test-retest reliability was evaluated by calculating Pearson correlation coefficients between the initial assessment and a second assessment no more than 14 days later. RESULTS: The web-based scan found 31 COVID-19-related symptoms; rankings of a 14-clinician expert panel reduced this list to 11 COVID-specific items to be added to the core MDASI. Time from literature scan start in March 2020 to instrument launch in May 2020 was 2 months. Psychometric analysis established the MDASI-COVID's reliability, known-group validity, and concurrent validity. CONCLUSIONS: We were able to rapidly develop and electronically launch a PRO measure of COVID-19 symptom burden in patients with cancer. Additional research is needed to confirm the content domain and predictive validity of the MDASI-COVID and define the symptom burden trajectory of COVID-19.

Development of a patient-reported outcome tool for assessing symptom burden during perioperative care in liver surgery: The MDASI-PeriOp-Hep.

PURPOSE: Based on the MD Anderson Symptom Inventory (MDASI), we developed a Patient-reported outcomes tool for hepatectomy perioperative care (MDASI-PeriOp-Hep). METHODS: To establish the content validity, we generated PeriOp-Hep-specific candidate items from qualitative interviews of patients (n = 30), and removed items that lacked clinical relevance on the basis of input from panels of patients, caregivers, and clinicians. The psychometric properties of the MDASI-PeriOp-Hep were validated (n = 150). The cognitive debriefing and clinical interpretability were assessed to confirm the ease of comprehension, relevance, and acceptability of the tool. RESULTS: Five symptoms specific to hepatectomy (abdominal bloating, tightness, or fullness; abdominal cramping; muscle weakness, instability, or vertigo; constipation; and incisional tightness) were identified as module items to form the MDASI-PeriOp-Hep. The Cronbach αs for symptoms and for interference were 0.898 and 0.861, respectively. The test-retest reliability was 0.887 for all 18 symptom severity items. Compared to other commonly used tools, correlation of MDASI-PeriOp-Hep scores to performance status (all, P < 0.001) and to the phase of perioperative care confirmed known-group validity. Convergent validity was excellent against other standard Patient-reported outcomes tools. Cognitive debriefing demonstrated that the MDASI-PeriOp-Hep was an easy to use and understandable tool. CONCLUSIONS: For integrating patient-reported outcomes in perioperative patient care, a procedure-specific tool is desirable. The MDASI-PeriOp-Hep is a valid, reliable, concise tool for measuring symptom severity and functional interference in patients undergoing liver surgery.

Anastrozole-associated joint pain and other symptoms in patients with breast cancer.

UNLABELLED: More than a third of breast cancer patients undergoing aromatase inhibitor (AI) treatment report joint pain. We conducted a longitudinal study to characterize the course of AI-induced joint pain and other symptoms and to identify potential predictors for developing these symptoms. Patients were recruited before AI initiation. The Brief Pain Inventory, M. D. Anderson Symptom Inventory, and a joint-pain questionnaire were administered at baseline and then biweekly for 1 year. Analysis included logistic regression, Cox models, and mixed-effects models. Of 47 patients assessed, 16 (34%) reported joint pain at least once. Median time to first report of joint pain was 7 weeks (range, 1-38). Baseline pain was the only predictor for both incidence of joint pain and time to first event. In the first 6 weeks, emergence of joint pain was associated with increase in general pain, fatigue, disturbed sleep, hot flashes, vaginal dryness, and decreased sexual activity. After week 6, having joint pain was associated with increase in general pain and with persistently high fatigue. Having AI-associated joint pain correlated with increase in or persistence of other symptoms likely related to AI therapy. Further research is needed to validate predictors of AI-associated symptoms. PERSPECTIVE: We demonstrate for the first time that AI-induced joint pain associates with development of other symptoms and that pretreatment pain level is a potential, measurable predictor of symptom development during treatment. Because baseline pain is easily assessed with a brief questionnaire, it can be applied clinically with minimal patient burden.

Automated symptom alerts reduce postoperative symptom severity after cancer surgery: a randomized controlled clinical trial.

PURPOSE: Patients receiving cancer-related thoracotomy are highly symptomatic in the first weeks after surgery. This study examined whether at-home symptom monitoring plus feedback to clinicians about severe symptoms contributes to more effective postoperative symptom control. PATIENTS AND METHODS: We enrolled 100 patients receiving thoracotomy for lung cancer or lung metastasis in a two-arm randomized controlled trial; 79 patients completed the study. After hospital discharge, patients rated symptoms twice weekly for 4 weeks via automated telephone calls. For intervention group patients, an e-mail alert was forwarded to the patient's clinical team for response if any of a subset of symptoms (pain, disturbed sleep, distress, shortness of breath, or constipation) reached a predetermined severity threshold. No alerts were generated for controls. Group differences in symptom threshold events were examined by generalized estimating equation modeling. RESULTS: The intervention group experienced greater reduction in symptom threshold events than did controls (19% v 8%, respectively) and a more rapid decline in symptom threshold events. The difference in average reduction in symptom interference between groups was -0.36 (SE, 0.078; P = .02). Clinicians responded to 84% of e-mail alerts. Both groups reported equally high satisfaction with the automated system and with postoperative symptom control. CONCLUSION: Frequent symptom monitoring with alerts to clinicians when symptoms became moderate or severe reduced symptom severity during the 4 weeks after thoracic surgery. Methods of automated symptom monitoring and triage may improve symptom control after major cancer surgery. These results should be confirmed in a larger study.