scholarly journals Median age at HPV infection among women in the United States: A model-based analysis informed by real-world data

2021 ◽  
Author(s):  
Vimalanand S Prabhu ◽  
Craig S Roberts ◽  
Smita Kothari ◽  
Linda Niccolai
Keyword(s):  
Natural History ◽  
Real World ◽  
Clinical Decision Making ◽  
Age Distribution ◽  
The United States ◽  
Hpv Infection ◽  
Real World Data ◽  
World Data ◽  
Model Based ◽  
Model Based Analysis

Abstract Background The US Advisory Committee for Immunization Practices (ACIP) recommended shared clinical decision-making for HPV vaccination of individuals aged 27 to 45 years (mid-adults) in June 2019. Determining the median age at causal HPV infection and CIN2+ diagnosis based on the natural history of HPV disease can help better understand the incidence of HPV infections and the potential benefits of vaccination in mid-adults. Methods Real-world data on CIN2+ diagnosis from the pre-vaccine era were sourced from a statewide surveillance registry in Connecticut. Age distribution of CIN2+ diagnosis in 2008 and 2009 was estimated. A discrete-event simulation model was developed to predict the age distribution of causal HPV infection. The optimal age distribution of causal HPV infection provided the best goodness-of-fit statistic to compare the predicted vs real-world age distribution of CIN2+ diagnosis. Results The median age at CIN2+ diagnosis from 2008 through 2009 in Connecticut was 28 years. The predicted median age at causal HPV infection was estimated to be 23.9 years. There was a difference of 5.2 years in the median age at acquisition of causal HPV infection and the median age at CIN2+ diagnosis. Conclusions Real-world data on CIN2+ diagnosis and model-based analysis indicate a substantial burden of infection and disease among women aged 27 years or older, which supports the ACIP recommendation to vaccinate some mid-adults . When natural history is known, this novel approach can also help determine the timing of causal infections for other commonly asymptomatic infectious diseases.

scholarly journals In-depth phenotyping for clinical stratification of Gaucher disease

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Simona D’Amore ◽  
Kathleen Page ◽  
Aimée Donald ◽  
Khadijeh Taiyari ◽  
Brian Tom ◽  
...  
Keyword(s):  
Real World ◽  
Gaucher Disease ◽  
Orthopaedic Surgery ◽  
Clinical Decision Making ◽  
Burden Of Illness ◽  
Fragility Fractures ◽  
Neurological Manifestations ◽  
Real World Data ◽  
World Data ◽  
Advanced Therapies

Abstract Background The Gaucher Investigative Therapy Evaluation is a national clinical cohort of 250 patients aged 5–87 years with Gaucher disease in the United Kingdom—an ultra-rare genetic disorder. To inform clinical decision-making and improve pathophysiological understanding, we characterized the course of Gaucher disease and explored the influence of costly innovative medication and other interventions. Retrospective and prospective clinical, laboratory and radiological information including molecular analysis of the GBA1 gene and comprising > 2500 variables were collected systematically into a relational database with banking of collated biological samples in a central bioresource. Data for deep phenotyping and life-quality evaluation, including skeletal, visceral, haematological and neurological manifestations were recorded for a median of 17.3 years; the skeletal and neurological manifestations are the main focus of this study. Results At baseline, 223 of the 250 patients were classified as type 1 Gaucher disease. Skeletal manifestations occurred in most patients in the cohort (131 of 201 specifically reported bone pain). Symptomatic osteonecrosis and fragility fractures occurred respectively in 76 and 37 of all 250 patients and the first osseous events occurred significantly earlier in those with neuronopathic disease. Intensive phenotyping in a subgroup of 40 patients originally considered to have only systemic features, revealed neurological involvement in 18: two had Parkinson disease and 16 had clinical signs compatible with neuronopathic Gaucher disease—indicating a greater than expected prevalence of neurological features. Analysis of longitudinal real-world data enabled Gaucher disease to be stratified with respect to advanced therapies and splenectomy. Splenectomy was associated with an increased hazard of fragility fractures, in addition to osteonecrosis and orthopaedic surgery; there were marked gender differences in fracture risk over time since splenectomy. Skeletal disease was a heavy burden of illness, especially where access to specific therapy was delayed and in patients requiring orthopaedic surgery. Conclusion Gaucher disease has been explored using real-world data obtained in an era of therapeutic transformation. Introduction of advanced therapies and repeated longitudinal measures enabled this heterogeneous condition to be stratified into obvious clinical endotypes. The study reveals diverse and changing phenotypic manifestations with systemic, skeletal and neurological disease as inter-related sources of disability.

scholarly journals Transmission of SARS-CoV-2 Considering Shared Chairs in Outpatient Dialysis: A Real-World Case-Control Study

2021 ◽  
Author(s):  
Ravi Thadhani ◽  
Joanna Willetts ◽  
Catherine Wang ◽  
John Larkin ◽  
Hanjie Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Case Control Study ◽  
The United States ◽  
Hemodialysis Patients ◽  
Case Control ◽  
Positive Patient ◽  
Sensitivity Analyses ◽  
Real World Data ◽  
World Data ◽  
Control Study

AbstractBackgroundSARS-CoV-2 is primarily transmitted through aerosolized droplets; however, the virus can remain transiently viable on surfaces.ObjectiveWe examined transmission within hemodialysis facilities, with a specific focus on the possibility of indirect patient-to-patient transmission through shared dialysis chairs.DesignWe used real-world data from hemodialysis patients treated between February 1st and June 8th, 2020 to perform a case-control study matching each SARS-CoV-2 positive patient (case) to a non-SARS-CoV-2 patient (control) in the same dialysis shift and traced back 14 days to capture possible exposure from chairs sat in by SARS-CoV-2 patients. Cases and controls were matched on age, sex, race, facility, shift date, and treatment count.Setting2,600 hemodialysis facilities in the United States.PatientsAdult (age ≥18 years) hemodialysis patients.MeasurementsConditional logistic regression models tested whether chair exposure after a positive patient conferred a higher risk of SARS-CoV-2 infection to the immediate subsequent patient.ResultsAmong 170,234 hemodialysis patients, 4,782 (2.8%) tested positive for SARS-CoV-2 (mean age 64 years, 44% female). Most facilities (68.5%) had 0 to 1 positive SARS-CoV-2 patient. We matched 2,379 SARS-CoV-2 positive cases to 2,379 non-SARS-CoV-2 controls; 1.30% (95%CI 0.90%, 1.87%) of cases and 1.39% (95%CI 0.97%, 1.97%) of controls were exposed to a chair previously sat in by a shedding SARS-CoV-2 patient. Transmission risk among cases was not significantly different from controls (OR=0.94; 95%CI 0.57 to 1.54; p=0.80). Results remained consistent in adjusted and sensitivity analyses.LimitationAnalysis used real-world data that could contain errors and only considered vertical transmission associated with shared use of dialysis chairs by symptomatic patients.ConclusionsThe risk of indirect patient-to-patient transmission of SARS-CoV-2 infection from dialysis chairs appears to be low.Primary Funding SourceFresenius Medical Care North America; National Institute of Diabetes and Digestive and Kidney Diseases (R01DK130067)

scholarly journals Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

2021 ◽  
Author(s):  
Gregory M Miller ◽  
Austin J Ellis ◽  
Rangaprasad Sarangarajan ◽  
Amay Parikh ◽  
Leonardo O Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Long Term Effects ◽  
Real World Data ◽  
World Data ◽  
Antiemetic Agent ◽  
Future Loss ◽  
Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

Analyzing treatment patterns and time to the next treatment in chronic lymphocytic leukemia real-world data using automated temporal phenotyping.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19512-e19512
Author(s):  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Yun Mai ◽  
Christopher Gilman ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Real World ◽  
Clinical Decision Making ◽  
Single Agent ◽  
Treatment Patterns ◽  
Lymphocytic Leukemia ◽  
Time Interval ◽  
Real World Data ◽  
World Data ◽  
Therapeutic Class

e19512 Background: Targeted therapy is an important treatment for chronic lymphocytic leukemia (CLL). However, optimal strategies for deploying small molecule inhibitors or antibody therapies in the real world are not well understood, largely due to a lack of outcomes data. We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. Here, the CLL treatment pattern and time to the next treatment (TTNT) were analyzed in real-world data (RWD) using patient electronic health records. Methods: We identified a CLL cohort with LOT from the Mount Sinai Data Warehouse (2003-2020). Each LOT consisted of either a single agent or combinations defined by NCCN CLL guidelines. We developed a natural language processing (NLP)-based temporal phenotyping approach to automatically identify the number of lines and therapeutic regimens. The sequence of treatment and time interval for each patient were derived from the systematic treatment data. Time to event analysis and multivariate (i.e., age, gender, race, other treatment patterns) Cox proportional hazard (CoxPH) models were used to analyze the patterns and predictors of TTNT. Results: Four hundred eleven CLL patients received 1 to 7 LOTs. Ibrutinib was the predominant 1st LOT (40.8% of patients) followed by anti-CD20-based antibody therapies and chemotherapy in 30.6 and 19.2% of patients, respectively, followed by Acalabrutinib, Venetoclax, and Idelalisib in 3.4, 2.7, and 0.7% of patients, respectively (Table 1). The 2nd to 5th LOT showed the same or similar trends. We next analyzed the TTNT in the 1st line of each therapeutic class. Acalabrutinib resulted in a longer median TTNT than Ibrutinib. Both Acalabrutinib and Ibrutinib showed longer TTNT compared to Venetoclax (median TTNTs were 742 and 598 vs. 373 days: HR = 0.23, p=0.015 and HR = 0.48, p=0.03, respectively). In addition, patients with age equal to or older than 65 showed longer TNNT (HR=0.16, p=0.016). Conclusions: Our result shows the potential of RWD usage in clinical decision making as real-world evidence reported here is consistent with results derived from clinical trial data. Linking this study to genetic data and other covariates affecting treatment outcomes may provide additional insights into the optimal sequences of the targeted therapies in CLL. Table 1: Therapeutic class and patient numbers (%) in each line.[Table: see text]

scholarly journals Estimating the Population Benefits and Costs of Rituximab Therapy in the United States from 1998 to 2013 Using Real-World Data

Medical Care ◽  
2016 ◽  
Vol 54 (4) ◽  
pp. 343-349 ◽  
Author(s):  
Mark D. Danese ◽  
Carolina M. Reyes ◽  
Michelle L. Gleeson ◽  
Marc Halperin ◽  
Sandra L. Skettino ◽  
...  
Keyword(s):  
United States ◽  
Real World ◽  
The United States ◽  
Real World Data ◽  
World Data ◽  
Benefits And Costs

Answering real-world questions using real-world data: Understanding dynamic treatment decisions and outcomes in metastatic colorectal cancer (mCRC).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18061-e18061
Author(s):  
Hui-Li Wong ◽  
Koen Degeling ◽  
Azim Jalali ◽  
Jeremy David Shapiro ◽  
Suzanne Kosmider ◽  
...  
Keyword(s):  
Simulation Model ◽  
Data Visualization ◽  
Real World ◽  
Clinical Decision Making ◽  
Discrete Event ◽  
Routine Practice ◽  
Real World Data ◽  
Specific Patient ◽  
World Data ◽  
Wide Range

e18061 Background: The wide range of possible combinations and sequences available for mCRC treatment presents a major challenge to clinicians, who need to determine the optimal approach for an individual patient or patient subset. In the absence of clinical trial evidence, real world data are an increasingly valuable resource that can be utilized not only to understand treatment patterns and outcomes in routine practice, but also to define an optimal treatment strategy for individual patients across multiple lines of therapy. Methods: Real world data from an Australian mCRC registry were used to develop an interactive data visualization tool that displays treatment variation, customizable to different levels of detail and specific patient subsets, based on patient and disease characteristics. Next, a discrete event simulation model was developed to predict progression-free (PFS) and overall survival (OS) for first line palliative treatment with doublet chemotherapy alone or with bevacizumab, based on data of 867 patients that were treated accordingly. Results: Of 2694 Australian patients enrolled, 2057 (76%) started 1st line treatment with chemotherapy and/or a biologic agent, 1087 (40%) and 428 (16%) received 2nd and 3rd line therapy, respectively. Combined, these 3 lines of treatment accounted for 733 unique sequences. After recoding treatment to the most intensive chemotherapy and the first exposed biologic, 472 unique sequences remained. In exploratory analyses, the simulation model estimated that median 1st line PFS (95% CI) of 219 (25%) patients could be improved from 175 (156, 199) to 269 days (247, 293) by targeting a different treatment. Conclusions: This was an initial exploration of the potential for data visualization and simulation modeling to inform understanding of practice in mCRC and to guide clinical decision making. Such tools allow clinicians and health system providers to define variation in practice patterns and to identify opportunities to improve care and outcomes. Ultimately, the aim is to improve the delivery of personalized cancer care, where other applications such as conditional survival and cost-effectiveness analyses may be useful.

Real-world outcomes of patients with BRAF-mutated mCRC treated in the United States.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 4030-4030
Author(s):  
Matthew Braithwaite ◽  
Christopher Duane Nevala-Plagemann ◽  
Kelsey Baron ◽  
Benjamin Haaland ◽  
Lisa M. Pappas ◽  
...  
Keyword(s):  
United States ◽  
Real World ◽  
Braf Mutation ◽  
The United States ◽  
Survival Outcomes ◽  
Prognostic Impact ◽  
Braf Mutations ◽  
Real World Data ◽  
World Data ◽  
Significant Difference

4030 Background: BRAF mutations portend a poor prognosis in metastatic colorectal cancer (mCRC). Recent trials have hypothesized that using more aggressive triplet-based chemotherapy regimens such as FOLFOXIRI in the frontline setting may improve outcomes in this patient population. In this study, we utilized real-world data to assess whether FOLFOXIRI is being used in the United States (US) and compared survival outcomes in BRAF mutated (BRAFmt) mCRC stratified by first line (1L) therapy. Methods: The nationwide Flatiron Health EHR-derived de-identified database was reviewed for patients diagnosed with mCRC between 2013 and 2018. Patients who had documented BRAF mutation testing and received a standard 1L therapy were included for analysis. Patients who did not have a visit or medication order within 90 days of metastatic diagnosis were excluded to ensure patients were engaged with care at the data-providing institution. Kaplan-Meier and Cox proportional hazard modeling were used to compare survival outcomes stratified by BRAF mutation status and 1L therapy received. Results: A total of 4,454 patients with documented BRAF mutational status were included, of which 3,988 (89.5%) were BRAF wild type (BRAFwt) and 466 (10.5%) were BRAFmt. Median OS was 15.4 months (mo) in the BRAFmt group compared to 28.1 mo in the BRAFwt group (HR 0.48, 95% CI 0.41- 0.56, p < 0.001). Only 3% (n = 16) of BRAFmt patients received 1L FOLFOXIRI +/- bevacizumab with a median OS of 13.8 mo compared to 15.5 mo in patients receiving a chemotherapy doublet (FOLFOX, CAPEOX, or FOLFIRI) +/- bevacizumab (95% CI 4.9 – not reached vs 14.3 – 19.0, p = 0.38). In BRAFmt patients, multivariate analysis (MVA) did not detect a significant improvement in OS with the use of FOLFIRI plus bevacizumab (HR 0.88, 95% CI 0.50-1.56, p = 0.67) or FOLFOX/CAPEOX plus bevacizumab (HR 0.89, 95% CI 0.59 – 1.34, p = 0.58) when compared to chemotherapy doublet alone. A MVA comparing 1L therapies in the BRAFwt group did not detect a significant improvement in OS with bevacizumab plus chemotherapy doublet compared to chemotherapy doublet alone. When stratified by 1L treatment regimen, similar proportions of BRAFmt patients received second line therapy. Conclusions: This analysis of real-world data confirms the negative prognostic impact of BRAF mutations in mCRC and suggests that FOLFOXIRI has not been widely adopted in the management of these patients in the US. We were unable to demonstrate any significant difference in OS of patients with BRAFmt mCRC based on type of 1L therapy received.

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