Reply to Callahan: Identifying "Real" Patients in the Real World.
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ABSTRACT:
SUBMITTER: Dempsey TM
PROVIDER: S-EPMC6961735 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Publications
Reply to Callahan: Identifying "Real" Patients in the Real World.
Dempsey Timothy M TM Limper Andrew H AH
American journal of respiratory and critical care medicine 20200101 2
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Project description: Not available
| S-EPMC6961738 | biostudies-literature
Project description:With the publication of the 2014 Focused Update of the Canadian Cardiovascular Society Guidelines for the Management of Atrial Fibrillation, the Canadian Cardiovascular Society Atrial Fibrillation Guidelines Committee has introduced a new triage and management algorithm; the so-called "CCS Algorithm". The CCS Algorithm is based upon expert opinion of the best available evidence; however, the CCS Algorithm has not yet been validated. Accordingly, the purpose of this study is to evaluate the performance of the CCS Algorithm in a cohort of real world patients.We compared the CCS Algorithm with the European Society of Cardiology (ESC) Algorithm in 172 hospital inpatients who are at risk of stroke due to non-valvular atrial fibrillation in whom anticoagulant therapy was being considered.The CCS Algorithm and the ESC Algorithm were concordant in 170/172 patients (99% of the time). There were two patients (1%) with vascular disease, but no other thromboembolic risk factors, which were classified as requiring oral anticoagulant therapy using the ESC Algorithm, but for whom ASA was recommended by the CCS Algorithm.The CCS Algorithm appears to be unnecessarily complicated in so far as it does not appear to provide any additional discriminatory value above and beyond the use of the ESC Algorithm, and its use could result in under treatment of patients, specifically female patients with vascular disease, whose real risk of stroke has been understated by the Guidelines.
| S-EPMC5497178 | biostudies-other
Project description:Background:A growing body of research suggests that major depressive disorder (MDD) is one of the most common psychiatric conditions associated with suicide ideation (SI). However, how a combination of easily accessible variables built a utility clinically model to estimate the probability of an individual patient with SI via machine learning is limited. Methods:We used the electronic medical record database from a hospital located in western China. A total of 1916 Chinese patients with MDD were included. Easily accessible data (demographic, clinical, and biological variables) were collected at admission (on the first day of admission) and were used to distinguish SI with MDD from non-SI using a machine learning algorithm (neural network). Results:The neural network algorithm distinguished 1356 out of 1916 patients translating into 70.08% accuracy (70.68% sensitivity and 67.09% specificity) and an area under the curve (AUC) of 0.76. The most relevant predictor variables in identifying SI from non-SI included free thyroxine (FT4), the total scores of Hamilton Depression Scale (HAMD), vocational status, and free triiodothyronine (FT3). Conclusion:Risk for SI among patients with MDD can be identified at an individual subject level by integrating demographic, clinical, and biological variables as possible as early during hospitalization (at admission).
| S-EPMC7061409 | biostudies-literature
Project description:BACKGROUND:Real-world data (RWD) play important roles in evaluating treatment effectiveness in clinical research. In recent decades, with the development of more accurate diagnoses and better treatment options, inpatient surgery for cervical degenerative disease (CDD) has become increasingly more common, yet little is known about the variations in patient demographic characteristics associated with surgical treatment. OBJECTIVE:This study aimed to identify the characteristics of surgical patients with CDD using RWD collected from electronic medical records. METHODS:This study included 20,288 inpatient surgeries registered from January 1, 2000, to December 31, 2016, among patients aged 18 years or older, and demographic data (eg, age, sex, admission time, surgery type, treatment, discharge diagnosis, and discharge time) were collected at baseline. Regression modeling and time series analysis were conducted to analyze the trend in each variable (total number of inpatient surgeries, mean age at surgery, sex, and average length of stay). A P value <.01 was considered statistically significant. The RWD in this study were collected from the Orthopedic Department at Peking University Third Hospital, and the study was approved by the institutional review board. RESULTS:Over the last 17 years, the number of inpatient surgeries increased annually by an average of 11.13%, with some fluctuations. In total, 76.4% (15,496/20,288) of the surgeries were performed in patients with CDD aged 41 to 65 years, and there was no significant change in the mean age at surgery. More male patients were observed, and the proportions of male and female patients who underwent surgery were 64.7% (13,126/20,288) and 35.3% (7162/20,288), respectively. However, interestingly, the proportion of surgeries performed among female patients showed an increasing trend (P<.001), leading to a narrowing sex gap. The average length of stay for surgical treatment decreased from 21 days to 6 days and showed a steady decline from 2012 onward. CONCLUSIONS:The RWD showed its capability in supporting clinical research. The mean age at surgery for CDD was consistent in the real-world population, the proportion of female patients increased, and the average length of stay decreased over time. These results may be valuable to guide resource allocation for the early prevention and diagnosis, as well as surgical treatment of CDD.
| S-EPMC7165306 | biostudies-literature
Project description:OBJECTIVE:The aim was to compare multimorbidity patterns identified with the two most commonly used methods: hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) in a large primary care database. Specific objectives were: (1) to determine whether choice of method affects the composition of these patterns and (2) to consider the potential application of each method in the clinical setting. DESIGN:Cross-sectional study. Diagnoses were based on the 263 corresponding blocks of the International Classification of Diseases version 10. Multimorbidity patterns were identified using HCA and EFA. Analysis was stratified by sex, and results compared for each method. SETTING AND PARTICIPANTS:Electronic health records for 408 994 patients with multimorbidity aged 45-64 years in 274 primary health care teams from 2010 in Catalonia, Spain. RESULTS:HCA identified 53 clusters for women, with just 12 clusters including at least 2 diagnoses, and 15 clusters for men, all of them including at least two diagnoses. EFA showed 9 factors for women and 10 factors for men. We observed differences by sex and method of analysis, although some patterns were consistent. Three combinations of diseases were observed consistently across sex groups and across both methods: hypertension and obesity, spondylopathies and deforming dorsopathies, and dermatitis eczema and mycosis. CONCLUSIONS:This study showed that multimorbidity patterns vary depending on the method of analysis used (HCA vs EFA) and provided new evidence about the known limitations of attempts to compare multimorbidity patterns in real-world data studies. We found that EFA was useful in describing comorbidity relationships and HCA could be useful for in-depth study of multimorbidity. Our results suggest possible applications for each of these methods in clinical and research settings, and add information about some aspects that must be considered in standardisation of future studies: spectrum of diseases, data usage and methods of analysis.
| S-EPMC5875653 | biostudies-literature
Project description: Not available
| S-EPMC10309504 | biostudies-literature
Project description:BackgroundDespite guideline recommendations, most patients with chronic obstructive pulmonary disease (COPD) do not undergo alpha-1 antitrypsin deficiency (AATD) testing and approximately 90% of people with AATD in the United States remain undiagnosed. This study sought to develop a predictive model using real-world data to improve detection of AATD-positive patients in the general COPD population.MethodsA predictive model using extreme gradient boosting was developed using the EVERSANA database, including longitudinal, patient-level medical claims, prescription claims, AATD-specific testing data, and electronic health records (EHR). The model was trained and then validated to predict AATD-positive status. Patients were coded as AATD positive based on the presence of any of the following criteria: (1) ≥2 AATD diagnosis codes in claims; (2) an AATD diagnosis code in the EHR; (3) a positive laboratory test for AATD; or (4) use of AATD-related medication. Over 500 variables were used to train the predictive model and >20 models were run to optimize the predictive power.ResultsA total of 13,585 AATD-positive patients and 7796 AATD-negative patients were included in the model. The inclusion of non-AATD laboratory test results was critical for defining cohorts and optimizing model prediction (e.g., respiratory comorbidities, and calcium, glucose, hemoglobin, and bilirubin levels). The final model yielded high predictive power, with an area under the receiver operating characteristic curve of 0.9.ConclusionPredictive modeling using real-world data is a sound approach for assessing AATD risk and useful for identifying COPD patients who should be confirmed by genetic testing. External validation is warranted to further assess the generalizability of these results.
| S-EPMC11925067 | biostudies-literature
Project description: Not available
| S-EPMC11335612 | biostudies-literature
Project description:Objective: To mine and analyze the adverse reaction signals of human serum albumin (HSA) using the FDA adverse event reporting system (FAERS) database for the safe clinical use of this drug. Methods: Data cleaning and analysis of adverse event reports in the FAERS database for a total of 76 quarters from Q1 2004 to Q4 2022 were performed using the reporting odds ratio (ROR), Medicines and Healthcare Products Regulatory Agency (MHRA), and Bayesian confidence propagation neural network (BCPNN). Gender-differentiated signal detection was used to investigate the gender differences in the occurrence of HSA adverse events. Results: Through a combination of three methods, a total of 535 adverse event reports were identified. These reports involved 1,885 cases of adverse reactions, with respiratory, thoracic, and mediastinal disorders, as well as general disorders and administration site conditions, as the most common. One noteworthy new signal was the occurrence of transfusion-related acute lung injury. Additionally, gender-differentiated signals were present, with females experiencing paraesthesia, hypertension, pulmonary oedema, loss of consciousness, and vomiting. Conclusion: This study has revealed that HSA poses a risk of causing transfusion-related acute lung injury. It has also been observed that adverse reactions, including paraesthesia, hypertension, pulmonary oedema, loss of consciousness, and vomiting, are more prevalent in females. These findings should be taken into account when using HSA in a clinical setting.
| S-EPMC10825956 | biostudies-literature
Project description:The present study aimed to investigate temporal trends in treatment patterns and prognostic factors for overall survival (OS) among patients with metastatic pancreatic cancer. From the Tokushukai REAl-world Data project, 1,093 patients with metastatic pancreatic cancer treated with gemcitabine, tegafur/gimeracil/oteracil (S-1), gemcitabine plus S-1, gemcitabine plus nab-paclitaxel, or fluorouracil, folic acid, oxaliplatin and irinotecan (FOLFIRINOX) between April 2010 and March 2020 were identified. Stratified/conventional Cox regression analyses were conducted to examine associations between patient- and tumor-related factors, study period, hospital volume, hospital type and first-line chemotherapy regimens. Overall, 846 patients were selected (503 male patients; median age, 70 years) after excluding ineligible patients. Over a median follow-up of 5.4 months, the median OS was 6.8 months (95% confidence interval, 6.3-7.4). The median OS for gemcitabine, S-1, gemcitabine plus S-1, gemcitabine plus nab-paclitaxel and FOLFIRINOX regimens was 5.9, 5.3, 7.7, 9.0 and 9.5 months, respectively. The median OS for 2010-2013, 2014-2017 and 2017-2020 was 6.2, 7.1 and 7.8 months, respectively. Performance status, body mass index and first-line chemotherapy regimens were identified to be significant prognostic factors. In summary, the real-world data indicated that standard care, including chemotherapy, for metastatic pancreatic cancer was widely used in hospitals throughout Japan and verified the survival benefits of gemcitabine plus nab-paclitaxel and FOLFIRINOX observed in prior clinical trials. This trial has been registered in the University Hospital Medical Information Network Clinical Trials Registry as UMIN000050590 on April 1, 2023.
| S-EPMC10636700 | biostudies-literature