Development of patient reported outcomes-based machine learning algorithm for the six-month mortality prediction in patients with advanced cancer.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 273-273
Author(s):  
Ishwaria Mohan Subbiah ◽  
Cai Xu ◽  
Sheng-Chieh Lu ◽  
Ali Haider ◽  
Ahsan Azhar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Advanced Cancer ◽  
Patient Reported Outcomes ◽  
Symptom Burden ◽  
Mortality Prediction ◽  
Assessment System ◽  
Shortness Of Breath ◽  
Extreme Gradient Boosting ◽  
Patient Reported ◽  
Systemic Therapies

273 Background: To date, studies of machine learning (ML) algorithms within oncology for mortality prediction have focused on structured electronic health record (EHR) data. Given the complex symptom burden of patients with advanced cancers, ML models may be better suited to identify patterns and interactions between symptom burden and outcomes compared to traditional statistical methods. To that end, in this study, we leverage the patient reported outcomes (PRO) data together with clinical EHR-based variables to assess the performance of ML algorithms to predict mortality in patients with advanced cancers. Methods: We randomly selected 689 patients with advanced cancer who had their first Palliative Care encounter between January 2012 and December 2017. 59 patients were lost to follow-up and were excluded from this analysis. The remaining cohort of 630 patients was split 4:1 randomly into a training and validation set to develop and test a supervised ML algorithm (Extreme Gradient Boosting [XGB] tree) to predict the 6-month mortality. Candidate variables for algorithm development included gender, age, ECOG performance status (PS), number of prior systemic therapies, and scores on the Edmonton Symptom Assessment System (ESAS)-FS, a 12-item PRO measure of physical and psychosocial symptom burden include the composite Physical Symptom Score (PHS), a sum of the physical ESAS symptoms (pain, fatigue, nausea, drowsiness, shortness of breath, appetite, wellbeing, sleep). Results: Overall, 630 patients were included in this 6-month mortality prediction; mean age 59 years, 354 (56%) female; 276 (44%) male. Variables with the most significant impact on the XGB tree mortality prediction were the ESAS symptoms of shortness of breath (1-AUC, 0.295), appetite, ESAS PHS, financial distress, age, and appetite as well as ECOG PS and number of prior systemic therapies. The XGB tree algorithm demonstrated the best overall prediction performance of 6-month mortality in the independent testing set, AUC 0.716 (95% CI 0.63 - 0.81), sensitivity 0.75 (95% CI 0.66 - 0.87), and a positive predictive value 0.67 (95% CI 0.57 - 0.79). Conclusions: Our ML model leveraged PRO-based assessment of symptom burden to correctly identify the majority of patients who died within 6 months. These models are uniquely positioned to not only automatically identify patients at high risk for short-term mortality but also the specific symptoms of concern for clinical intervention. Such models can be applied to available clinical and PRO data to facilitate clinical decision-making. Futures studies on improving model performance with the inclusion of interventions to modify symptom burden are in design.

A digital oncology patient-reported outcomes platform: Building innovative electronic patient symptom assessments in epic mychart to improve the quality of life and survival of patients with advanced cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18312-e18312 ◽  
Author(s):  
Sidharth Anand ◽  
Anne Margaret Walling ◽  
Sarah F. DAmbruoso ◽  
Neil Wenger ◽  
Jennifer Singer ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Overall Survival ◽  
Advanced Cancer ◽  
Patient Reported Outcomes ◽  
Best Practice ◽  
Response Rate ◽  
Assessment System ◽  
Patient Reported ◽  
Practice Advisory

e18312 Background: Establishing a system to monitor patient reported outcomes (“PRO”) has been demonstrated to be essential for a well-functioning cancer system. Studies have shown that routine collection of PROs allows providers to address medical issues earlier and impacts a patient’s overall survival. Unmet needs for symptom management are prevalent in the cancer population, especially patients with advanced cancer. Approximately 35% of UCLA Hematology-Oncology patients with advanced cancer in 2016 presented to Emergency Rooms for symptom-related complaints such as nausea, pain, constipation, dehydration, and fatigue. We hypothesize that the creation of an electronic PRO platform through EPIC MyChart will ensure patients receive timely evaluation of their symptoms, resulting in improved quality of life, and decreased ER and hospital utilization. Methods: We developed an innovative PRO platform through Epic MyChart along with a Best Practice Advisory alert system to identify patients at risk for worsening symptoms, ER visits, and inpatient admissions. We then built an electronic version of the Edmonson Symptoms Assessment System, which providers can push to patients through Epic MyChart, with results stored within the Flowsheets section of Epic. We also built a passive alert using Epic’s Best Practice Advisory (“BPA”) system, to notify providers when a patient’s MyChart ESAS Assessment Scores have exceeded a defined threshold. Results: Preliminary data from surveys sent to a series of advanced cancer patients seen in an outpatient palliative oncology clinic over 1 month, demonstrated a 100% response rate (6/7) surveys completed when sent one week prior to patient’s being seeing in clinic, and 17% response rate (1/6) when sent two to three weeks prior to clinic visit. The average total ESAS score reported was 40, with average individual score of 4/10 for any given symptom. Conclusions: We will implement this electronic PRO platform in multiple oncology clinics at UCLA, and measure provider and patient satisfaction, completion rates, and monitor outcomes such as ED visits and inpatient admissions. We hope this system will lead to an overall survival benefit. This project demonstrates the potential of developing innovative PRO platforms through Epic MyChart and the importance of clinical workflows in the implementation process.

scholarly journals Potentially Avoidable Hospital Readmissions in Patients With Advanced Cancer

2019 ◽  
Vol 15 (5) ◽  
pp. e420-e427 ◽  
Author(s):  
P. Connor Johnson ◽  
Yian Xiao ◽  
Risa L. Wong ◽  
Sara D'Arpino ◽  
Samantha M.C. Moran ◽  
...  
Keyword(s):  
Advanced Cancer ◽  
Odds Ratio ◽  
Hospital Readmissions ◽  
Symptom Burden ◽  
Symptom Assessment ◽  
Physical Symptom ◽  
Assessment System ◽  
Primary Reason ◽  
Nonlinear Mixed Effects Models ◽  
Patient Reported

PURPOSE: Patients with cancer often prefer to avoid time in the hospital; however, data are lacking on the prevalence and predictors of potentially avoidable readmissions (PARs) among those with advanced cancer. METHODS: We enrolled patients with advanced cancer from September 2, 2014, to November 21, 2014, who had an unplanned hospitalization and assessed their patient-reported symptom burden (Edmonton Symptom Assessment System) at the time of admission. For 1 year after enrollment, we reviewed patients’ health records to determine the primary reason for every hospital readmission and we classified readmissions as PARs using adapted Graham’s criteria. We examined predictors of PARs using nonlinear mixed-effects models with binomial distribution. RESULTS: We enrolled 200 (86.2%) of 232 patients who were approached. For these 200 patients, we reviewed 277 total hospital readmissions and identified 108 (39.0%) of these as PARs. The most common reasons for PARs were premature discharge from a prior hospitalization (30.6%) and failure of timely follow-up (28.7%). PAR hospitalizations were more likely than non-PAR hospitalizations to experience symptoms as the primary reason for admission (28.7% v 13.0%; P = .001). We found that married patients were less likely to experience PARs (odds ratio, 0.30; 95% CI, 0.15 to 0.57; P < .001) and that those with a higher physical symptom burden were more likely to experience PARs (odds ratio, 1.03; 95% CI, 1.01 to 1.05; P = .012). CONCLUSION: We observed that a substantial proportion of hospital readmissions are potentially avoidable and found that patients’ symptom burdens predict PARs. These findings underscore the need to assess and address the symptom burden of hospitalized patients with advanced cancer in this highly symptomatic population.

scholarly journals Measuring patient-reported outcomes to improve cancer care in Canada: an analysis of provincial survey data

2018 ◽  
Vol 25 (2) ◽  
pp. 176 ◽  
Author(s):  
K. Tran ◽  
S. Zomer ◽  
J. Chadder ◽  
C. Earle ◽  
S. Fung ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
Cancer Care ◽  
Patient Reported Outcomes ◽  
Symptom Burden ◽  
Symptom Assessment ◽  
Physical Symptoms ◽  
Assessment System ◽  
System Level ◽  
Care Providers ◽  
Patient Reported

Patient-reported outcomes measures (proms) are an important component of the shift from disease-centred to person-centred care. In oncology, proms describe the effects of cancer and its treatment from the patient perspective and ideally enable patients to communicate to their providers the physical symptoms and psychosocial concerns that are most relevant to them. The Edmonton Symptom Assessment System–revised (esas-r) is a commonly used and validated tool in Canada to assess symptoms related to cancer. Here, we describe the extent to which patient reported outcome programs have been implemented in Canada and the severity of symptoms causing distress for patients with cancer.As of April 2017, 8 of 10 provinces had implemented the esas-r to assess patient-reported outcomes. Data capture methods, the proportion of cancer treatment sites that have implemented the esas-r, and the time and frequency of screening vary from province to province. From October 2016 to March 2017 in the 8 reporting provinces, 88.0% of cancer patients were screened for symptoms. Of patients who reported having symptoms, 44.3% reported depression, with 15.5% reporting moderate-to-high levels; 50.0% reported pain, with 18.6% reporting moderate-to-high levels; 56.2% reported anxiety, with 20.4% reporting moderate-to-high levels; and 75.1% reported fatigue, with 34.4% reporting moderate-to-high levels.There are some notable areas in which the implementation of proms could be improved in Canada. Findings point to a need to increase the number of cancer treatment sites that screen all patients for symptoms; to standardize when and how frequently patients are screened across the country; to screen patients for symptoms during all phases of their cancer journey, not just during treatment; and to assess whether giving cancer care providers real-time patient-reported outcomes data has led to appropriate interventions that reduce the symptom burden and improve patient outcomes. Continued measurement and reporting at the system level will allow for a better understanding of progress in proms activity over time and of the areas in which targeted quality improvement efforts could ensure that patient symptoms and concerns are being addressed.

Individualized cancer follow-up supported by electronic Patient-Reported Outcomes: Development and evaluation (Preprint)

2020 ◽  
Author(s):  
Sissel Ravn ◽  
Henriette Vind Thaysen ◽  
Lene Seibaek ◽  
Victor Jilbert Verwaal ◽  
Lene Hjerrild Iversen
Keyword(s):  
Ct Scan ◽  
Advanced Cancer ◽  
Patient Reported Outcomes ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Structured Interviews ◽  
European Organization ◽  
Individualized Care ◽  
Patient Reported

BACKGROUND Cancer survivors experience unmet needs during follow-up. Besides recurrence, a follow-up includes detection of late side effects, rehabilitation, palliation and individualized care. OBJECTIVE We aimed to describe the development and evaluate the feasibility of an intervention providing individualized cancer follow-up supported by electronic patient-reported outcomes (e-PRO). METHODS The study was carried out as an interventional study at a Surgical and a Gynecological Department offering complex cancer surgery and follow-up for advanced cancer. The e-PRO screened for a priori defined clinical important symptoms and needs providing individualized follow-up. We included following questionnaires in the e-PRO; the general European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30 and the EORTC validated for colorectal and ovarian cancer patients. To support individualization, we included three prioritized issues of the patient’s preference in each e-PRO. The response-algorithm was aggregated based on the severity of the patient’s response. To ensure the sensitivity of the e-PRO, we performed semi-structured interviews with five patients. All clinicians (surgeons and gynecologists) performing the consultations reviewed the e-PRO. The evaluation was divided in two, 1)The feasibility was assessed by a)Patients’ response rate of the e-PRO, b)Number of follow-up visits documenting the use of e-PRO and c)Patients’ prioritized issues prior to the consultation(‘yes’ / ‘no’), and after the follow-up 2)Patients assessment of a)The need and purpose of the follow-up visit and b)the support provided during the follow-up visit. RESULTS In total, 187 patients were included in the study, of which 73%(n=136/187) patients responded to the e-PRO and were subjected to an individualized follow-up. The e-PRO was documented as applied in 79% of the follow-up visits. In total, 23% of the prioritized issues did not include a response. Stratified by time since surgery, significantly more patients did not fill out a prioritized issue had a follow-up >6 months since surgery. In total, 72 % follow-up visits were evaluated to be necessary in order to discuss the outcome of the CT scan, symptoms, and/or prioritized issues. Contrary, 19% of the follow-up visits were evaluated to be necessary only to discuss the result of the CT scan. A range from 19.3–56.3% of patients assessed the follow-up visit to provide support with respect to physical (42% of patients), mental (56%), sexual (19%) or dietary (27%) issues. Further, a range from 34–60% of the patients reported that they did not need support regarding physical (43% of patients), mental (34%), sexual (63%) or dietary (57%) issues. CONCLUSIONS An individualized follow-up based on e-PRO is feasible, and support most patients surgically treated for advanced cancer. However, results indicate that follow-up based on e-PRO may not be beneficial for all patients and circumstances. A thorough cost-benefit analysis may be warranted before implementation in routine clinic.

scholarly journals Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Accuracy Score ◽  
Patient Reported ◽  
Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

scholarly journals P1.10-005 Generation of Symptom Burden Patient-Reported Outcomes for Patients with Lung Cancer

2017 ◽  
Vol 12 (11) ◽  
pp. S2024
Author(s):  
L. Williams ◽  
C. Cleeland ◽  
O. Bamidele ◽  
G. Simon
Keyword(s):  
Lung Cancer ◽  
Patient Reported Outcomes ◽  
Symptom Burden ◽  
Patient Reported

Patient reported outcomes in patients with desmoid type fibromatosis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11574-11574
Author(s):  
VIKAS GARG ◽  
Sameer Rastogi ◽  
Adarsh Barwad ◽  
Rambha Panday ◽  
Sandeep Kumar Bhoriwal ◽  
...  
Keyword(s):  
Patient Reported Outcomes ◽  
Symptom Burden ◽  
Benign Neoplasm ◽  
Anxiety And Depression ◽  
Appendicular Skeleton ◽  
Healthy Controls ◽  
Symptom Scale ◽  
Cross Sectional ◽  
Patient Reported ◽  
The Impact

11574 Background: Desmoid type fibromatosis (DTF) is a rare benign neoplasm with infiltrative growth and high local recurrences. Due to long disease course, unpredictable growth pattern, and low mortality, using only survival outcomes may be inappropriate. In this study we assessed the impact of DTF on health related quality of life (HRQoL). Methods: This was a cross-sectional study done in patients with DTF. The study participants were asked to fill the EORTC QLQ-C30, GAD-7 and PHQ- 9 q uestionnaires to assess HRQoL, anxiety and depression . Outcomes were also compared with healthy controls. Results: 204 subjects (102 DTF patients and 102 healthy controls) were recruited. Study parameters have been summarized in Table. Appendicular skeleton (limbs + girdle) was most commonly involved in 59 % patients and abdominal wall or mesentery was involved in 22.5 %. Patients have received median of 2 lines of therapy. 54 % patients were currently on sorafenib and 41 % were under active surveillance. Mean global health status in DTF patient 65.58 ± 22.64, was significantly lower than healthy controls. Similarly, DTF patients scored low on all functional scales except cognitive functioning. Symptom scale showed significantly higher symptom burden of fatigue, pain, insomnia and financial difficulties. Anxiety & depression was observed in 39.22 % and 50 % of DTF patients respectively. DTF patients had higher rates of mild, moderate and severe anxiety and depression compared to healthy controls. No difference was observed based on site of disease. Conclusions: DTF patients have significant symptom burden, poor functioning, and heightened anxiety and depression. Patient reported outcomes should be routinely used to assess treatment efficacy in DTF patients.[Table: see text]

scholarly journals Prescreening of Patient-reported Symptoms using the Edmonton Symptom Assessment System (ESAS) in Outpatient Palliative Cancer Care

2019 ◽  
Author(s):  
Garden Lee ◽  
Han Sang Kim ◽  
Si Won Lee ◽  
Eun Hwa Kim ◽  
Bori Lee ◽  
...  
Keyword(s):  
Palliative Care ◽  
Cancer Care ◽  
Severe Symptom ◽  
Symptom Burden ◽  
Symptom Assessment ◽  
Assessment System ◽  
Advanced Cancer Patients ◽  
Edmonton Symptom Assessment System ◽  
Patient Reported

Abstract Background: Although early palliative care is associated with a better quality of life and improved outcomes in end-of-life cancer care, the criteria of palliative care referral are still elusive. Methods: We collected patient-reported symptoms using the Edmonton Symptom Assessment System (ESAS) at the baseline, first, and second follow-up visit. The ESAS evaluates ten symptoms: pain, fatigue, nausea, depression, anxiety, drowsiness, dyspnea, sleep disorder, appetite, and wellbeing. A total of 71 patients were evaluable, with a median age of 65 years, male (62%), and the Eastern Cooperative Oncology Group (ECOG) performance status distribution of 1/2/3 (28%/39%/33%), respectively. Results: Twenty (28%) patients had moderate/severe symptom burden with the mean ESAS ≥5. Interestingly, most of the patients with moderate/severe symptom burdens (ESAS ≥5) had globally elevated symptom expression. While the mean ESAS score was maintained in patients with mild symptom burden (ESAS<5; 2.7 at the baseline; 3.4 at the first follow-up; 3.0 at the second follow-up; P =0.117), there was significant symptom improvement in patients with moderate/severe symptom burden (ESAS≥5; 6.5 at the baseline; 4.5 at the first follow-up; 3.6 at the second follow-up; P <0.001). Conclusions: Advanced cancer patients with ESAS ≥5 may benefit from outpatient palliative cancer care. Prescreening of patient-reported symptoms using ESAS can be useful for identifying unmet palliative care needs in advanced cancer patients.

Assessing Symptom Burden and Depression in Subjects With Chronic Respiratory Insufficiency

2021 ◽  
pp. 082585972110495
Author(s):  
Heidi A. Rantala ◽  
Sirpa Leivo-Korpela ◽  
Lauri Lehtimäki ◽  
Juho T. Lehto
Keyword(s):  
Respiratory Insufficiency ◽  
Advanced Disease ◽  
Depression Scale ◽  
Symptom Burden ◽  
Depression Score ◽  
Assessment System ◽  
Dry Mouth ◽  
Depression Symptom ◽  
Shortness Of Breath ◽  
Chronic Respiratory Insufficiency

Objectives: Patients with chronic respiratory insufficiency suffer from advanced disease, but their overall symptom burden is poorly described. We evaluated the symptoms and screening of depression in subjects with chronic respiratory insufficiency by using the Edmonton symptom assessment system (ESAS). Methods: In this retrospective study, 226 subjects with chronic respiratory insufficiency answered the ESAS questionnaire measuring symptoms on a scale from 0 (no symptoms) to 10 (worst possible symptom), and the depression scale (DEPS) questionnaire, in which the cut-off point for depressive symptoms is 9. Results: The most severe symptoms measured with ESAS (median [interquartile range]) were shortness of breath 4.0 (1.0-7.0), dry mouth 3.0 (1.0-7.0), tiredness 3.0 (1.0-6.0), and pain on movement 3.0 (0.0-6.0). Subjects with a chronic obstructive pulmonary disease as a cause for chronic respiratory insufficiency had significantly higher scores for shortness of breath, dry mouth, and loss of appetite compared to others. Subjects with DEPS ≥9 reported significantly higher symptom scores in all ESAS categories than subjects with DEPS <9. The area under the receiver operating characteristic curve for ESAS depression score predicting DEPS ≥9 was 0.840 ( P < .001). If the ESAS depression score was 0, there was an 89% probability of the DEPS being <9, and if the ESAS depression score was ≥4, there was an 89% probability of the DEPS being ≥9. The relation between ESAS depression score and DEPS was independent of subjects’ characteristics and other ESAS items. Conclusions: Subjects with chronic respiratory insufficiency suffer from a high symptom burden due to their advanced disease. The severity of symptoms increases with depression and 4 or more points in the depression question of ESAS should lead to a closer diagnostic evaluation of depression. Symptom-centered palliative care including psychosocial aspects should be early integrated into the treatment of respiratory insufficiency.

Impact of a tiered discharge opioid algorithm on prescriptions and patient-reported outcomes after open gynecologic surgery

2021 ◽  
pp. ijgc-2021-002674
Author(s):  
Sarah Huepenbecker ◽  
Robert Tyler Hillman ◽  
Maria D Iniesta ◽  
Tsun Chen ◽  
Katherine Cain ◽  
...  
Keyword(s):  
Patient Reported Outcomes ◽  
Enhanced Recovery ◽  
Symptom Burden ◽  
Equivalent Dose ◽  
Hospital Length Of Stay ◽  
Gynecologic Surgery ◽  
Post Discharge ◽  
Opioid Prescribing ◽  
Patient Reported ◽  
Morphine Equivalent

ObjectiveTo compare discharge opioid refills, prescribed morphine equivalent dose and quantity, and longitudinal patient-reported outcomes before and after implementation of a tiered opioid prescribing algorithm among women undergoing open gynecologic surgery within an enhanced recovery after surgery program.MethodsWe compared opioid prescriptions, clinical outcomes, and patient-reported outcomes among 273 women. Post-discharge symptom burden was collected up to 42 days after discharge using the validated 27-item MD Anderson Symptom Inventory and analyzed using linear mixed effects models and Kaplan–Meier curves for symptom recovery.ResultsAmong 113 pre-implementation and 160 post-implementation patients there was no difference in opioid refills (9.7% vs 11.3%, p=0.84). The post-implementation cohort had a significant reduction in median morphine equivalent dose (112.5 mg vs 225 mg, p<0.01), with no difference in median hospital length of stay (3 days vs 3 days, p=1.0) or 30-day readmission rate (9.4% vs 7.1%, p=0.66). There was no difference in patient-reported pain between the pre- and post-implementation cohorts on the day of discharge (severity 4.93 vs 5.14, p=0.53) or in any patient-reported symptoms, interference measures, or composite scores by post-discharge day 7. The median recovery time for most symptoms was 7 days, except for pain (14 days), fatigue (18 days), and physical interference (21 days), with no differences between cohorts.ConclusionsAfter implementation of a tiered opioid prescribing algorithm, the quantity and dose of discharge opioids prescribed decreased with no change in post-operative refills and without negatively impacting patient-reported symptom burden or interference, which can be used to educate and reassure patients and providers.

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