Real-world data (RWD) and patients reported outcomes (PRO) in breast cancer (BC): Physical, emotional side effects (S/E), financial toxicity (FT), and complementary usage (CM) relations.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18060-e18060
Author(s):  
Daniel A. Vorobiof ◽  
Eliran Malki ◽  
Irad Deutsch ◽  
Lior Hasid
Keyword(s):  
Strong Relationship ◽  
Emotional Symptoms ◽  
Real World Data ◽  
Health Records ◽  
Younger Age ◽  
High Incidence ◽  
Knowledge Of Cancer ◽  
Absence From Work ◽  
Stage 1 ◽  
Patients Reported Outcomes

e18060 Background: RWD denotes data accumulated via electronic health records as well as cutting edge technology paired with consumer mobile devices. Artificial intelligence (AI) incorporates the use of algorithms and machine learning (ML) software to analyze complex medical data, improving the knowledge of cancer journeys. PRO on 256 BC patients (pts) survey repliers, users of Belong.life, a worldwide social media application for cancer pts, are presented. Methods: From 11/2018 to 01/2019, 256 pts replied to a 37 questions’ survey which included information regarding clinical and emotional S/E, FT and CM use. Results: 98 pts (38%) were < 50 years of age, 158 (62%) > 50 years. Most of the pts (163, 64%) were diagnosed within 2017-8. Most pts had Stage 1 (83 pts,32%) and 2 (94 pts,37%) and 71 pts (28%) had Stage 3-4. 154 (61%) had neo-adjuvant anthracyclines-based treatments, followed by a taxane (docetaxel/paclitaxel). 149 of 256 pts (58%) reported clinical S/E: nausea and vomiting in 77 pts (52%), fatigue in 63 pts (42%), hair loss in 42 pts (28%) and body pain and neuropathy in 13 pts each (8.8%). 90 pts (35%) described various emotional symptoms: depression in 32 pts (35.6%), anxiety 13 (14.4%), ‘up and down’ feelings in 20 pts (22 %). FT was experienced by 100/208 pts (48%). FT was most prevalent in ages 35-50 (51/85pts, 60%) than in 51-65 (41/100pts, 41%). Main reported causes in both groups were loss or absence from work (33% and 51%) and treatment copayments (40% and 21%). CM use was reported by 42/256 pts (16.4%). Most common CM were nutritional, multivitamins, supplements and CBD oil use. ML documented a strong relationship between BC recurrence in 55/256 pts (21.5%), FT in 25/55 pts (45.5%) and CM use in 9/25 pts (36%) vs no FT in 3/30 pts (10%). Conclusions: 256 BC users of the Belong application reported on clinical and emotional S/E, FT and CM use. The high incidence of emotional S/E stresses the need for individualized attention. FT was most prevalent in the younger age group (35-50yrs) due to work loss and treatment copayments. A significant relationship was determined by ML techniques on those patients experiencing BC recurrence, FT and CM use.

scholarly journals Leveraging Real-World Data for the Selection of Relevant Eligibility Criteria for the Implementation of Electronic Recruitment Support in Clinical Trials

2021 ◽  
Vol 12 (01) ◽  
pp. 017-026
Author(s):  
Georg Melzer ◽  
Tim Maiwald ◽  
Hans-Ulrich Prokosch ◽  
Thomas Ganslandt
Keyword(s):  
Data Warehouse ◽  
Real World ◽  
Chart Review ◽  
Patient Recruitment ◽  
Real World Data ◽  
Eligibility Criteria ◽  
Health Records ◽  
World Data ◽  
Paper Chart ◽  
Data Elements

Abstract Background Even though clinical trials are indispensable for medical research, they are frequently impaired by delayed or incomplete patient recruitment, resulting in cost overruns or aborted studies. Study protocols based on real-world data with precisely expressed eligibility criteria and realistic cohort estimations are crucial for successful study execution. The increasing availability of routine clinical data in electronic health records (EHRs) provides the opportunity to also support patient recruitment during the prescreening phase. While solutions for electronic recruitment support have been published, to our knowledge, no method for the prioritization of eligibility criteria in this context has been explored. Methods In the context of the Electronic Health Records for Clinical Research (EHR4CR) project, we examined the eligibility criteria of the KATHERINE trial. Criteria were extracted from the study protocol, deduplicated, and decomposed. A paper chart review and data warehouse query were executed to retrieve clinical data for the resulting set of simplified criteria separately from both sources. Criteria were scored according to disease specificity, data availability, and discriminatory power based on their content and the clinical dataset. Results The study protocol contained 35 eligibility criteria, which after simplification yielded 70 atomic criteria. For a cohort of 106 patients with breast cancer and neoadjuvant treatment, 47.9% of data elements were captured through paper chart review, with the data warehouse query yielding 26.9% of data elements. Score application resulted in a prioritized subset of 17 criteria, which yielded a sensitivity of 1.00 and specificity 0.57 on EHR data (paper charts, 1.00 and 0.80) compared with actual recruitment in the trial. Conclusion It is possible to prioritize clinical trial eligibility criteria based on real-world data to optimize prescreening of patients on a selected subset of relevant and available criteria and reduce implementation efforts for recruitment support. The performance could be further improved by increasing EHR data coverage.

scholarly journals Rationale and design of the Novel Uses of adaptive Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) pragmatic adaptive randomized trial: a trial protocol

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Julie C. Lauffenburger ◽  
Thomas Isaac ◽  
Lorenzo Trippa ◽  
Punam Keller ◽  
Ted Robertson ◽  
...  
Keyword(s):  
Older Adults ◽  
Usual Care ◽  
Behavioral Science ◽  
Inappropriate Prescribing ◽  
The Novel ◽  
Care Providers ◽  
Health Records ◽  
Electronic Health ◽  
Stage 1

Abstract Background The prescribing of high-risk medications to older adults remains extremely common and results in potentially avoidable health consequences. Efforts to reduce prescribing have had limited success, in part because they have been sub-optimally timed, poorly designed, or not provided actionable information. Electronic health record (EHR)-based tools are commonly used but have had limited application in facilitating deprescribing in older adults. The objective is to determine whether designing EHR tools using behavioral science principles reduces inappropriate prescribing and clinical outcomes in older adults. Methods The Novel Uses of Designs to Guide provider Engagement in Electronic Health Records (NUDGE-EHR) project uses a two-stage, 16-arm adaptive randomized pragmatic trial with a “pick-the-winner” design to identify the most effective of many potential EHR tools among primary care providers and their patients ≥ 65 years chronically using benzodiazepines, sedative hypnotic (“Z-drugs”), or anticholinergics in a large integrated delivery system. In stage 1, we randomized providers and their patients to usual care (n = 81 providers) or one of 15 EHR tools (n = 8 providers per arm) designed using behavioral principles including salience, choice architecture, or defaulting. After 6 months of follow-up, we will rank order the arms based upon their impact on the trial’s primary outcome (for both stages): reduction in inappropriate prescribing (via discontinuation or tapering). In stage 2, we will randomize (a) stage 1 usual care providers in a 1:1 ratio to one of the up to 5 most promising stage 1 interventions or continue usual care and (b) stage 1 providers in the unselected arms in a 1:1 ratio to one of the 5 most promising interventions or usual care. Secondary and tertiary outcomes include quantities of medication prescribed and utilized and clinically significant adverse outcomes. Discussion Stage 1 launched in October 2020. We plan to complete stage 2 follow-up in December 2021. These results will advance understanding about how behavioral science can optimize EHR decision support to improve prescribing and health outcomes. Adaptive trials have rarely been used in implementation science, so these findings also provide insight into how trials in this field could be more efficiently conducted. Trial registration Clinicaltrials.gov (NCT04284553, registered: February 26, 2020)

HUBUNGAN INTENSITAS KEBISINGAN DENGAN TEKANAN DARAH PADA PEKERJA AREA WORKSHOP

2020 ◽  
Vol 9 (1) ◽  
pp. 18-26
Author(s):  
Mariman Tjendera ◽  
Isramilda Isramilda
Keyword(s):  
Blood Pressure ◽  
Noise Intensity ◽  
Circulatory System ◽  
Sampling Technique ◽  
Strong Relationship ◽  
Normal Blood ◽  
Normal Blood Pressure ◽  
Exclusion Criteria ◽  
Cross Sectional ◽  
Stage 1

Noise in the workplace is often a separate problem for the workforce so that it can cause mental-emotional disturbance as well as the heart and circulatory system. According to the 2013 Basic Health Research, the prevalence of high blood pressure in Indonesia at the age of ≥18 years is 25.8%. So the researchers wanted to find a relationship between noise intensity and blood pressure. This research method was observational analytic with a cross-sectional approach conducted at PT. Bintang Intipersada Shipyard, Batam City. The sampling technique was a total sampling with a population of 100 workers in 2018 and obtained results of 61 workers determined by inclusion and exclusion criteria. The results of the study were analyzed with a frequency distribution tabulated and tested with the Pearson Product Moment Test. The results of this study worker who were exposed to noise intensity ≤85 dBA had a normal blood pressure of 8 (36.5%) people, in workers who were exposed to noise intensity ≤85 dBA had blood pressure with Pre-hypertension as many as 12 (54.5% ) people, for workers exposed to noise intensity ≤85 dBA have 1 (4.5%) blood pressure with Stage 1 Hypertension, workers who are exposed to noise intensity ≤85 dBA have blood pressure with Stage 2 Hypertension as much as 1 (4.5 %) person. While workers who are exposed to noise intensity> 85 dBA have normal blood pressure of 0 (0%) people, workers who are exposed to noise intensity> 85 dBA have blood pressure with Pre-hypertension as much as 2 (5.1%) people, workers are exposed to noise intensity> 85 dBA had blood pressure with Stage 1 Hypertension as many as 14 (35.9%) people, then workers exposed to noise intensity> 85 dBA had blood pressure with Stage 2 Hypertension as many as 23 (59%) workers. The results of the Pearson Product Moment analysis value of p = 0,000 <α 0.05, there is a relationship between noise intensity and blood pressure with the magnitude of the correlation coefficient (r) which is 0.795 meaning, there is a strong relationship. Based on this study it can be concluded that there is a significant relationship between noise intensity and blood pressure.

Assessing function of electronic health records for real-world data generation

2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Data Generation ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

scholarly journals Clinical and PSG Characteristics of Children with Mild OSA and Respiratory Events Terminated Predominantly with Arousal

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yunxiao Wu ◽  
Li Zheng ◽  
Panting Wu ◽  
Yufen Tang ◽  
Zhifei Xu ◽  
...  
Keyword(s):  
Life Questionnaire ◽  
Sleep Related Breathing Disorders ◽  
Arousal Index ◽  
Respiratory Events ◽  
Younger Age ◽  
Obstructive Sleep ◽  
Respiratory Event ◽  
Mild Obstructive Sleep Apnea ◽  
Stage 1

Objective. To analyze the clinical and polysomnographic characteristics in children with mild OSA and respiratory events terminated predominantly with arousal. Methods. Children aged 3–10 yrs who had mild obstructive sleep apnea (OSA) were enrolled. All children underwent polysomnography, and patients’ data were collected by using sleep-related breathing disorders (SRBD) questionnaire and OSA-18 quality of life questionnaire. Results. In total, five hundred and seventy-seven children were eligible. Children in arousal predominant group were younger and showed a lower rate of male and obesity. Compared with that of the nonarousal predominant group, the total arousal index, arousal index related to respiratory event, the percentage of NREM stage 1 (N1%), the fraction of respiratory events that were hypopnea, and the mean and minimum oxygen saturation in the arousal predominant group were significantly greater. The percentage of NREM stage 3 (N%), index of obstructive, central, mixed apnea, the fraction of respiratory events that were obstructive, and central and mixed apnea were significantly lower in arousal predominant group. Conclusion. Children with mild OSA in the arousal predominant group had specific characteristics, including younger age, lower rate of male and obesity, worse sleep architecture, higher rates of hypopnea events, and better oxygenation. This trial is registered with NCT02447614.

scholarly journals Improving EWMA Plans for Detecting Unusual Increases in Poisson Counts

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
R. S. Sparks ◽  
T. Keighley ◽  
D. Muscatello
Keyword(s):  
Public Health ◽  
Time Series ◽  
Steady State ◽  
Regression Model ◽  
Moving Average ◽  
Outbreak Detection ◽  
Exponential Weights ◽  
Health Records ◽  
High Incidence ◽  
Weighted Moving Average

Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average (EWMA) plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.

scholarly journals Failure to Thrive and its Risk Factors in 0-24 Months Children in Bojnurd City of Iran during 2008-2013

2020 ◽  
Author(s):  
Hossein Lashkardoost ◽  
Saeid Doaei ◽  
Zohreh Akbari ◽  
Fatemeh Mashkooti ◽  
Ebrahim Hosseinzadeh ◽  
...  
Keyword(s):  
Sampling Method ◽  
Social Economic ◽  
Failure To Thrive ◽  
Health Centers ◽  
Capital City ◽  
Related Factor ◽  
Chi Square ◽  
Health Records ◽  
High Incidence ◽  
Type Of Delivery

Background: Failure to thrive (FTT) is a global problem and one of the most common health problems in childhood that involves many other social, economic, and cultural factors. Considering the adverse effects of FTT in the future of children, we studied FTT and its related factor in children under the age of 2 years in Bojnurd (the capital city of North Khorasan province, Iran). Methods: This study was a Retrospective cohort study on 1000 health records, born in 2008-2013. Stratified sampling method was applied and the data were collected using a checklist in the health centers. Finally, data were analyzed using Chi-square, Multiple logistic regression, and independent t-test in SPSS19 software. Significant level was set at 5%. Results: Incidence of FTT was calculated as 443 children (44.3%) in the children's first two years of life. A significant relationship was observed between FTT in children and head circumference disorders at birth (p=0.001), maternal age at delivery (p=0.01), mother's education level (OR=0.4   CI95% [0.2-0.8]  p=0.012), type of delivery (OR=0.5 CI95% [0.4-0.7]   p<0.001), unspecified gestational age (OR=3.6   CI95% [1.3-10.08   p=0.015]), and pregnancy under the age of 18 (OR=2.4   CI95% [1.1-5.3]  p=0.02).  Conclusion: Considering the high incidence of FTT in children, increasing awareness about timely feeding, promoting households' health, preventing and controlling infectious diseases should be improved

scholarly journals Designing and evaluating contextualized drug–drug interaction algorithms

JAMIA Open ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Eric Chou ◽  
Richard D Boyce ◽  
Baran Balkan ◽  
Vignesh Subbian ◽  
Andrew Romero ◽  
...  
Keyword(s):  
Tertiary Care ◽  
Care Facility ◽  
Clinical Decision ◽  
Clinical Situation ◽  
Urban Hospital ◽  
Real World Data ◽  
Health Records ◽  
Specific Patient ◽  
Tertiary Care Facility ◽  
Alerting Systems

Abstract Objective Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug–drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient’s clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms that take advantage of patient context available in electronic health records (EHRs) data. Methods Data on the rate at which DDI alerts were triggered but for which no action was taken over a 3-month period (override rates) from a single tertiary care facility were used to identify DDIs that were considered a high-priority for contextualized alerting. A panel of DDI experts developed algorithms that incorporate drug and patient characteristics that affect the relevance of such warnings. The algorithms were then implemented as computable artifacts, validated using a synthetic health records data, and tested over retrospective data from a single urban hospital. Results Algorithms and computable knowledge artifacts were developed and validated for a total of 8 high priority DDIs. Testing on retrospective real-world data showed the potential for the algorithms to reduce alerts that interrupt clinician workflow by more than 50%. Two algorithms (citalopram/QT interval prolonging agents, and fluconazole/opioid) showed potential to filter nearly all interruptive alerts for these combinations. Conclusion The 8 DDI algorithms are a step toward addressing a critical need for DDI alerts that are more specific to patient context than current commercial alerting systems. Data commonly available in EHRs can improve DDI alert specificity.

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