PSY51 Epidemiology and Treatment Patterns of inhibitor Hemophilia in Russia: Patient-reported Data

Abstract Introduction There is currently an increased demand for elective orthopaedic surgery. However, due to the ever-growing financial, time and resource limitations, there is a pressing need to identify those who would benefit most from surgery but with the lowest risk of complications. Comorbidities are a fundamental factor in this decision and the traditional way to ascertain this is through medical record data abstraction during pre-operative assessment. However, this can be time consuming and expensive. We therefore set out to establish whether patient reported comorbidities are reliable as a principal source of information. Method Searches were performed on PubMed and Medline, and citations independently screened. Included studies were published between 2010 to 2020 assessing the reliability of at least one patient reported comorbidity against their medical record or clinical assessment as gold standard. Cohen’s kappa coefficient values were grouped into systems and a meta-analysis performed comparing the reliability between studies. Results Meta-analysis data showed poor-to-moderate reliability for diseases in cardiovascular, musculoskeletal, neurological and respiratory systems as well as for malignancy and depression. Endocrine diseases showed good-to-excellent reliability. Factors found to affect the concordance included sex, age, ethnicity, education, living alone, marital status, number or severity of comorbidities, mental health and disability. Conclusion Our study showed poor-to-moderate reliability for all systems except endocrine, consisting of thyroid disease and diabetes mellitus, which demonstrated good-to-excellent reliability. Although patient reported data is useful and can facilitate a complete pre-operative overview of the patient, it is not reliable enough to be used as a standalone measure.

Download Full-text

What is the optimal outcome after endoscopic sinus surgery in the treatment of chronic rhinosinusitis? A consultation of Canadian experts

Journal of Otolaryngology - Head and Neck Surgery ◽

10.1186/s40463-021-00519-9 ◽

2021 ◽

Vol 50 (1) ◽

Author(s):

Nadim Saydy ◽

Sami P. Moubayed ◽

Marie Bussières ◽

Arif Janjua ◽

Shaun Kilty ◽

...

Keyword(s):

Chronic Rhinosinusitis ◽

Endoscopic Sinus Surgery ◽

Patient Reported Outcome ◽

Sinus Surgery ◽

Structured Interviews ◽

Subjective Data ◽

Operative Outcomes ◽

Patient Reported ◽

Definition Of Success ◽

Reported Data

Abstract Objectives Many experts feel that in the absence of well-defined goals for success, they have an easier time identifying failure. As success ought to not be defined only by absence of failure, we aimed to define optimal outcomes for endoscopic sinus surgery (ESS) in chronic rhinosinusitis (CRS) by obtaining expert surgeon perspectives. Methods A total of 12 surgeons participated in this targeted consultation. Face to face semi-structured interviews were performed with expert surgeons in the field of CRS and ESS. General impressions and personal definitions of acceptable operative success and optimal operative outcomes were compiled and summarized. Results According to an expert survey, patients’ main objectives are an improvement in their chief complain, a general improvement in quality of life (QoL), and a better overall symptomatic control. The most important aspects of endoscopy for defining a successful intervention were an adequate mucus circulation, a healthy mucosa, minimal edema, and patency of all explored cavities or ostia. In the assessment of surgical outcomes, it was determined that both objective and patient reported data must be carefully examined, with more attention given to subjective outcomes. Conclusions According to data gathered from a Canadian expert consultation, a definition of success must be based on both subjective data and nasal endoscopy. We propose to define an acceptable outcome as either a subjective improvement of at least the minimal clinically improvement difference of a validated patient reported outcome questionnaire, along with a satisfactory endoscopic result (1) or a complete subjective resolution with a sub-optimal endoscopy (2). Graphical abstract

Download Full-text

Predicting Obstetric Disease With Machine Learning Applied to Patient-Reported Data (Preprint)

10.2196/preprints.11766 ◽

2018 ◽

Cited By ~ 1

Author(s):

Danielle Bradley ◽

Erin Landau ◽

Adam Wolfberg ◽

Alex Baron

Keyword(s):

Machine Learning ◽

At Risk ◽

Mobile Apps ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Obstetric Outcomes ◽

Patient Reported ◽

Data Points ◽

Reported Data

BACKGROUND The rise of highly engaging digital health mobile apps over the past few years has created repositories containing billions of patient-reported data points that have the potential to inform clinical research and advance medicine. OBJECTIVE To determine if self-reported data could be leveraged to create machine learning algorithms to predict the presence of, or risk for, obstetric outcomes and related conditions. METHODS More than 10 million women have downloaded Ovia Health’s three mobile apps (Ovia Fertility, Ovia Pregnancy, and Ovia Parenting). Data points logged by app users can include information about menstrual cycle, health history, current health status, nutrition habits, exercise activity, symptoms, or moods. Machine learning algorithms were developed using supervised machine learning methodologies, specifically, Gradient Boosting Decision Tree algorithms. Each algorithm was developed and trained using anywhere from 385 to 5770 features and data from 77,621 to 121,740 app users. RESULTS Algorithms were created to detect the risk of developing preeclampsia, gestational diabetes, and preterm delivery, as well as to identify the presence of existing preeclampsia. The positive predictive value (PPV) was set to 0.75 for all of the models, as this was the threshold where the researchers felt a clinical response—additional screening or testing—would be reasonable, due to the likelihood of a positive outcome. Sensitivity ranged from 24% to 75% across all models. When PPV was adjusted from 0.75 to 0.52, the sensitivity of the preeclampsia prediction algorithm rose from 24% to 85%. When PPV was adjusted from 0.75 to 0.65, the sensitivity of the preeclampsia detection or diagnostic algorithm increased from 37% to 79%. CONCLUSIONS Algorithms based on patient-reported data can predict serious obstetric conditions with accuracy levels sufficient to guide clinical screening by health care providers and health plans. Further research is needed to determine whether such an approach can improve outcomes for at-risk patients and reduce the cost of screening those not at risk. Presenting the results of these models to patients themselves could also provide important insight into otherwise unknown health risks.

Download Full-text

NCOG-42. SYMPTOM ONSET TO TIME OF DIAGNOSIS IN PRIMARY CENTRAL NERVOUS SYSTEM TUMOR PATIENTS: A REVIEW OF FINDINGS FROM THE NOB-NHS

Neuro-Oncology ◽

10.1093/neuonc/noab196.632 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi161-vi161

Author(s):

Matthew Lindsley ◽

Elizabeth Vera ◽

Alvina Acquaye ◽

Nicole Briceno ◽

Anna Choi ◽

...

Keyword(s):

Central Nervous System ◽

Nervous System ◽

Healthcare Providers ◽

Healthcare Setting ◽

Tumor Location ◽

Low Grade ◽

Central Nervous System Tumor ◽

Tumor Patients ◽

Patient Reported ◽

Reported Data

Abstract Prior reports suggest the low prevalence of primary central nervous system (PCNS) tumors and the healthcare setting where patients seek care can contribute to diagnostic delays, potentially affecting prognosis. This descriptive report highlights findings from patient-reported data at presentation collected from a sample of 623 PCNS tumor patients. Participants were White (88%), males (56%), median age at diagnosis 41 (2-79) with high grade (HG) (66%) brain tumors (BT) (89%). Among BT patients, 30% reported ≥ 3 concurrent symptoms at presentation including headaches (40%), seizures (30%), and memory problems or difficulty with balance/walking (20% each). Over half (57%) had symptoms for < 6 months before diagnosis and 60% presented to the Emergency Room. Sixty-five percent of HG BT patients had symptoms for < 6 months prior to diagnosis compared to low grade (LG) tumors (40%) and had surgery in < 1 month from presentation (68% vs 51%, p < 0.01). More HG BT patients presented with weakness in the arms/legs than LG BT (14% vs 8%). Among spine tumor (ST) patients, 45% reported ≥ 3 concurrent symptoms at presentation including back pain (65%), sensory changes (45%), and weakness (40%). Almost half (46%) were symptomatic for > 1 year before diagnosis, presented in an outpatient clinic (64%) with 41% having surgery < 1 month from presentation. Younger (40% vs 16%) and HG ST patients (56% vs 21%) more often reported symptoms for < 6 months before diagnosis. HG ST patients more often presented to Emergency Rooms (67% vs 25%) and had surgery < 1 month from presentation (60% vs 36%). Further analysis of symptom presentation and clinical course is ongoing. Tumor location, grade, patient age and healthcare setting were associated with the time from clinical presentation to diagnosis. Development of aids providing guidance on diagnostic evaluation/treatment to front-line healthcare providers is warranted.

Download Full-text

717-P: Real-World, Patient-Reported Data on Training and Use of 670G Hybrid Closed-Loop (HCL) among Children and Adults with Type 1 Diabetes (T1D)

Diabetes ◽

10.2337/db21-717-p ◽

2021 ◽

Vol 70 (Supplement 1) ◽

pp. 717-P

Author(s):

COLLEEN BAUZA ◽

STEPHANIE DUBOSE ◽

ALANDRA VERDEJO ◽

ROY BECK ◽

RICHARD M. BERGENSTAL ◽

...

Keyword(s):

Type 1 Diabetes ◽

Real World ◽

Closed Loop ◽

Patient Reported ◽

Reported Data

Download Full-text

The Development and Initial Findings of A Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease (SPARC IBD)

Inflammatory Bowel Diseases ◽

10.1093/ibd/izab071 ◽

2021 ◽

Author(s):

Laura E Raffals ◽

Sumona Saha ◽

Meenakshi Bewtra ◽

Cecile Norris ◽

Angela Dobes ◽

...

Keyword(s):

Inflammatory Bowel Disease ◽

Clinical Data ◽

Bowel Disease ◽

Patient Reported Outcomes ◽

Data Entry ◽

The United States ◽

Predictors Of Response ◽

Patient Reported ◽

Inflammatory Bowel ◽

Reported Data

Abstract Background Clinical and molecular subcategories of inflammatory bowel disease (IBD) are needed to discover mechanisms of disease and predictors of response and disease relapse. We aimed to develop a study of a prospective adult research cohort with IBD (SPARC IBD) including longitudinal clinical and patient-reported data and biosamples. Methods We established a cohort of adults with IBD from a geographically diverse sample of patients across the United States with standardized data and biosample collection methods and sample processing techniques. At enrollment and at time of lower endoscopy, patient-reported outcomes (PRO), clinical data, and endoscopy scoring indices are captured. Patient-reported outcomes are collected quarterly. The quality of clinical data entry after the first year of the study was assessed. Results Through January 2020, 3029 patients were enrolled in SPARC, of whom 66.1% have Crohn’s disease (CD), 32.2% have ulcerative colitis (UC), and 1.7% have IBD-unclassified. Among patients enrolled, 990 underwent colonoscopy. Remission rates were 63.9% in the CD group and 80.6% in the UC group. In the quality study of the cohort, there was 96% agreement on year of diagnosis and 97% agreement on IBD subtype. There was 91% overall agreement describing UC extent as left-sided vs extensive or pancolitis. The overall agreement for CD behavior was 83%. Conclusion The SPARC IBD is an ongoing large prospective cohort with longitudinal standardized collection of clinical data, biosamples, and PROs representing a unique resource aimed to drive discovery of clinical and molecular markers that will meet the needs of precision medicine in IBD.

Download Full-text

The Rationale for Collecting Patient-Reported Symptoms during Routine Chemotherapy

American Society of Clinical Oncology Educational Book ◽

10.14694/edbook_am.2014.34.161 ◽

2014 ◽

pp. 161-165 ◽

Cited By ~ 13

Author(s):

Ethan Basch

Keyword(s):

Clinical Practice ◽

Symptom Control ◽

Data Interpretation ◽

Data Representation ◽

Routine Care ◽

Self Report ◽

Multiple Systems ◽

Patient Reported ◽

American Society ◽

Reported Data

Patient-reported outcomes (PROs) such as symptoms and functional status are commonly measured in clinical trials. There is increasing interest to integrate PROs into routine clinical practice, for example during chemotherapy or postoperatively. There are several rationales for this. First, patient self-reporting improves patient-clinic communication, symptom detection, and symptom control. Second, patient-reported data may be used for quality assessment. Third, aggregated patient-reported data can be informative in comparative-effectiveness research (CER). Of particular interest is an approach that employs electronic collection of PROs with interfaces to the electronic health record (EHR) and clinician alerts for concerning symptoms. Multiple systems have been developed in oncology with these characteristics. Barriers to implementation exist, such as cost, logistics, EHR interfacing, data representation, and data interpretation, but have been largely overcome. Missing data remain a concern, but backup data collection strategies can bring self-report compliance rates up to about 85% in unselected routine care patients with advanced cancers. Overall, including patient self-reporting in routine care enhances quality of care and patient satisfaction, and is expected to become more common in the future. American Society for Clinical Oncology (ASCO) has several ongoing initiatives to develop standards and clinical practice tools in this area.

Download Full-text