Medical Records Based Real-World Study of Cetuximab in Combination with FOLFIRI Regimen as First-Line Chemotherapy and Subsequent Cetuximab-based Maintenance Therapy for the Maintenance Treatment of Patients with RAS and BRAF Wild-Type Unresectable Metastatic Colorectal Cancer
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ABSTRACT:
DISEASE(S): Colorectal Cancer
PROVIDER: 2701129 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Intervention1: NIL: NIL
Primary outcome(s): Clinical profile & tumor characteristics of mCRC patients
Usage pattern of targeted therapies (bevacizumab, cetuximab, and panitumumab) in mCRC patients
Overall Response Rate (ORR)
Clinical Benefit Rate (CBR)
Progression Free Survival (PFS)
Duration of Response (DoR)
Overall Survival (OS)
Adverse drug reactions associated with targeted therapyTimepoint: Baseline and follow up as per routine clinical practice
| 96797 | ecrin-mdr-crc
Project description:Clopidogrel efficacy is influenced by genetic variation of cytochrome P450 (CYP)2C19, however, few studies have considered patients who have a stroke. We used electronic medical records (EMRs) linked to a bioresource to examine real-world implications of clopidogrel pharmacogenetics in stroke. Patients hospitalized for any arterial thrombo-occlusive (ATO) event who subsequently redeemed clopidogrel prescriptions in the community were entered into the study (n = 651). During 24-month follow-up, the primary endpoint of recurrent ATO or death occurred in 299 patients (46%). CYP2C19*2 loss-of-function allele carriers had an increased risk (hazard ratio (HR) = 1.29; 95% confidence interval (CI) = 1.04-1.59; P = 0.019). In the ischemic stroke subgroup (n = 94), the estimate of risk was greater (HR = 2.23; 95% CI = 1.17-4.24; P = 0.015), which was further supported by a meta-analysis of available studies. In conclusion, we have demonstrated the clinical impact of CYP2C19*2 on clopidogrel efficacy using a purely EMR approach. This suggests that the risk in the ischemic stroke population may be particularly high.
| S-EPMC5813097 | biostudies-literature
Project description:Interventions: Case series:Nil
Primary outcome(s): survival;latest date of follow-up;date of surgery
Study Design: Factorial
| 2689050 | ecrin-mdr-crc
Project description:BackgroundIntegrative medicine is a form of medicine that combines practices and treatments from alternative medicine with conventional medicine. The diagnosis in integrative medicine involves the clinical diagnosis based on modern medicine and syndrome pattern diagnosis. Electronic medical records (EMRs) are the systematized collection of patients health information stored in a digital format that can be shared across different health care settings. Although syndrome and sign information or relative information can be extracted from the EMR and content texts can be mapped to computability vectors using natural language processing techniques, application of artificial intelligence techniques to support physicians in medical practices remains a major challenge.ObjectiveThe purpose of this study was to investigate model-based reasoning (MBR) algorithms for the clinical diagnosis in integrative medicine based on EMRs and natural language processing. We also estimated the associations among the factors of sample size, number of syndrome pattern type, and diagnosis in modern medicine using the MBR algorithms.MethodsA total of 14,075 medical records of clinical cases were extracted from the EMRs as the development data set, and an external test data set consisting of 1000 medical records of clinical cases was extracted from independent EMRs. MBR methods based on word embedding, machine learning, and deep learning algorithms were developed for the automatic diagnosis of syndrome pattern in integrative medicine. MBR algorithms combining rule-based reasoning (RBR) were also developed. A standard evaluation metrics consisting of accuracy, precision, recall, and F1 score was used for the performance estimation of the methods. The association analyses were conducted on the sample size, number of syndrome pattern type, and diagnosis of lung diseases with the best algorithms.ResultsThe Word2Vec convolutional neural network (CNN) MBR algorithms showed high performance (accuracy of 0.9586 in the test data set) in the syndrome pattern diagnosis of lung diseases. The Word2Vec CNN MBR combined with RBR also showed high performance (accuracy of 0.9229 in the test data set). The diagnosis of lung diseases could enhance the performance of the Word2Vec CNN MBR algorithms. Each group sample size and syndrome pattern type affected the performance of these algorithms.ConclusionsThe MBR methods based on Word2Vec and CNN showed high performance in the syndrome pattern diagnosis of lung diseases in integrative medicine. The parameters of each group's sample size, syndrome pattern type, and diagnosis of lung diseases were associated with the performance of the methods.Trial registrationClinicalTrials.gov NCT03274908; https://clinicaltrials.gov/ct2/show/NCT03274908.
| S-EPMC7781803 | biostudies-literature
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:IntroductionThe effectiveness of telmisartan has been reported in Indian clinical trials; however, real-world data are limited. We aimed to provide real-world evidence regarding the effectiveness of telmisartan as monotherapy or in combination with other antihypertensive drugs (AHDs) in Indian patients with essential hypertension.MethodsElectronic medical record data of adult patients diagnosed with essential hypertension (≥ 140/90 mmHg) and who were prescribed telmisartan as mono- or add-on therapy were retrospectively analyzed. Patients were classified according to the number of AHD classes prescribed on initiating telmisartan. Change in systolic and diastolic blood pressure (SBP and DBP) after a month of treatment and the proportion of patients who achieved treatment goals according to the 2018 European Society of Cardiology/European Society of Hypertension guidelines were evaluated.ResultsA majority (90.6%) of the 1304 patients included in the study were on telmisartan monotherapy or telmisartan + 1 AHD. The mean (95% confidence interval [CI]) change in the telmisartan monotherapy group was SBP (-13.3 [-14.6, -12.0] mmHg) and DBP (-7.2 [-7.9, -6.5] mmHg), and the mean (95% CI) change in the telmisartan + 1 AHD group was SBP (-10.8 [-13.1, -8.5] mmHg) and DBP (-6.5 [-7.7, -5.3] mmHg) (P < 0.001 for all). SBP and DBP goals were achieved by 35.9% and 47.3% of patients on telmisartan monotherapy and by 35.9% and 46.8% of patients on telmisartan + 1 AHD. Among patients with comorbid diabetes, the mean (95% CI) change in the telmisartan monotherapy group was SBP (-13.3 [-15.0, -11.6] mmHg) and DBP (-7.3 [-8.2, -6.5] mmHg), and the mean (95% CI) change in the telmisartan + 1 AHD group was SBP (-13.0 [-16.5, -9.5] mmHg) and DBP (-6.9 [-8.7, -5.1] mmHg) (P < 0.001 for all). SBP and DBP goals were achieved by 31.7% and 39.7% of patients on telmisartan monotherapy and by 31.9% and 41.8% of patients on telmisartan + 1 AHD.ConclusionTelmisartan may be a good candidate for blood pressure control in Indian patients with essential hypertension and comorbidities.
| S-EPMC8126538 | biostudies-literature
Project description:Real-time automated continuous sampling of electronic medical record data may expeditiously identify patients at risk for death and enable prompt life-saving interventions. We hypothesized that a real-time electronic medical record-based alert could identify hospitalized patients at risk for mortality.An automated alert was developed and implemented to continuously sample electronic medical record data and trigger when at least 2 of 4 systemic inflammatory response syndrome criteria plus at least one of 14 acute organ dysfunction parameters was detected. The systemic inflammatory response syndrome and organ dysfunction alert was applied in real time to 312,214 patients in 24 hospitals and analyzed in 2 phases: training and validation datasets.In the training phase, 29,317 (18.8%) triggered the alert and 5.2% of such patients died, whereas only 0.2% without the alert died (unadjusted odds ratio 30.1; 95% confidence interval, 26.1-34.5; P < .0001). In the validation phase, the sensitivity, specificity, area under the curve, and positive and negative likelihood ratios for predicting mortality were 0.86, 0.82, 0.84, 4.9, and 0.16, respectively. Multivariate Cox-proportional hazard regression model revealed greater hospital mortality when the alert was triggered (adjusted hazards ratio 4.0; 95% confidence interval, 3.3-4.9; P < .0001). Triggering the alert was associated with additional hospitalization days (+3.0 days) and ventilator days (+1.6 days; P < .0001).An automated alert system that continuously samples electronic medical record data can be implemented, has excellent test characteristics, and can assist in the real-time identification of hospitalized patients at risk for death.
| S-EPMC4916370 | biostudies-literature
Project description:Purpose: We evaluated clinical outcomes of patients with FGFR3-altered metastatic UC treated with ICB and investigate the underlying immunogenomic mechanisms of response and resistance Methods: 103 patients with metastatic UC treated with ICB at a single academic medical center from 2014-2018 were identified. Clinical annotation for demographics and cancer outcomes, as well as somatic DNA and RNA sequencing were performed. Objective response rate to ICB, progression-free survival, and overall survival was compared between patients with FGFR3 alterations and those without. RNA expression, including molecular subtyping and T cell receptor clonality, was also compared between FGFR3-altered and non-altered patients Results: Our findings from this dataset confirm that FGFR3-altered (n=17) and wild type (n=86) bladder cancers are equally responsive to ICB (12 vs 19%, p=0.73). Moreover, we demonstrate that despite being less inflamed, FGFR3-altered tumors have equivalent T cell receptor (TCR) diversity and that the balance of a CD8 T cell gene expression signature to immune suppressive features is an important determinant of ICB response Conclusions: Our work in a real world dataset validates prior observations from clinical trials but also extends this prior work to demonstrate that FGFR3-altered and wild type tumors have equivalent TCR diversity and that the balance of effector T cell to immune suppression signals are an important determinant of ICB response.
2021-06-08 | GSE176307 | GEO
Project description:PurposeTo determine the pattern of dose adjustment of recombinant human follicle-stimulating hormone alfa (r-hFSH-alfa) during ovarian stimulation (OS) for assisted reproductive technology (ART) in a real-world setting.MethodsThis was an observational, retrospective analysis of data from an electronic de-identified medical records database including 39 clinics in the USA. Women undergoing OS for ART (initiated 2009-2016) with r-hFSH-alfa (Gonal-f® or Gonal-f RFF Redi-ject®) were included. Assessed outcomes were patients' baseline characteristics and dosing characteristics/cycle.ResultsOf 33,962 ART cycles, 13,823 (40.7%) underwent dose adjustments: 23.4% with ≥1 dose increase, 25.4% with ≥1 dose decrease, and 8.1% with ≥1 increase and ≥1 decrease. Patients who received dose adjustments were younger (mean [SD] age 34.8 [4.58] years versus 35.9 [4.60] years, p<0.0001) and had lower BMI (25.1 [5.45] kg/m2 versus 25.5 [5.45] kg/m2, p<0.0001) than those who received a constant dose. The proportion of patients with non-normal ovarian reserve was 38.4% for those receiving dose adjustment versus 51.9% for those with a constant dose. The mean (SD) number of dose changes/cycle was 1.61 (0.92) for cycles with any dose adjustment, 1.72 (1.03) for cycles with ≥1 dose increase, 2.77 (1.00) for cycles with ≥1 dose increase and ≥1 decrease (n=2,755), and 1.88 (1.03) for cycles with ≥1 dose decrease.ConclusionsDose adjustment during OS is common in clinical practice in the USA and occurred more often in younger versus older patients, those with a high versus non-normal ovarian reserve or those with ovulation disorders/polycystic ovary syndrome versus other primary diagnoses of infertility.
| S-EPMC8696034 | biostudies-literature
Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.
| S-EPMC6604278 | biostudies-literature