Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records.
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ABSTRACT:
SUBMITTER: Sun Y
PROVIDER: S-EPMC8261679 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveElectronic health records (EHR) data have become a central data source for clinical research. One concern for using EHR data is that the process through which individuals engage with the health system, and find themselves within EHR data, can be informative. We have termed this process informed presence. In this study we use simulation and real data to assess how the informed presence can impact inference.Materials and methodsWe first simulated a visit process where a series of biomarkers were observed informatively and uninformatively over time. We further compared inference derived from a randomized control trial (ie, uninformative visits) and EHR data (ie, potentially informative visits).ResultsWe find that only when there is both a strong association between the biomarker and the outcome as well as the biomarker and the visit process is there bias. Moreover, once there are some uninformative visits this bias is mitigated. In the data example we find, that when the "true" associations are null, there is no observed bias.DiscussionThese results suggest that an informative visit process can exaggerate an association but cannot induce one. Furthermore, careful study design can, mitigate the potential bias when some noninformative visits are included.ConclusionsWhile there are legitimate concerns regarding biases that "messy" EHR data may induce, the conditions for such biases are extreme and can be accounted for.
| S-EPMC6857502 | biostudies-literature
Project description:Electronic health-records (EHR) are increasingly used by epidemiologists studying disease following surveillance testing to provide evidence for screening intervals and referral guidelines. Although cost-effective, undiagnosed prevalent disease and interval censoring (in which asymptomatic disease is only observed at the time of testing) raise substantial analytic issues when estimating risk that cannot be addressed using Kaplan-Meier methods. Based on our experience analysing EHR from cervical cancer screening, we previously proposed the logistic-Weibull model to address these issues. Here we demonstrate how the choice of statistical method can impact risk estimates. We use observed data on 41,067 women in the cervical cancer screening program at Kaiser Permanente Northern California, 2003-2013, as well as simulations to evaluate the ability of different methods (Kaplan-Meier, Turnbull, Weibull and logistic-Weibull) to accurately estimate risk within a screening program. Cumulative risk estimates from the statistical methods varied considerably, with the largest differences occurring for prevalent disease risk when baseline disease ascertainment was random but incomplete. Kaplan-Meier underestimated risk at earlier times and overestimated risk at later times in the presence of interval censoring or undiagnosed prevalent disease. Turnbull performed well, though was inefficient and not smooth. The logistic-Weibull model performed well, except when event times didn't follow a Weibull distribution. We have demonstrated that methods for right-censored data, such as Kaplan-Meier, result in biased estimates of disease risks when applied to interval-censored data, such as screening programs using EHR data. The logistic-Weibull model is attractive, but the model fit must be checked against Turnbull non-parametric risk estimates.
| S-EPMC5930038 | biostudies-literature
Project description:BackgroundClinical features from electronic health records (EHRs) can be used to build a complementary tool to predict coronary artery disease (CAD) susceptibility.ObjectivesThe purpose of this study was to determine whether an EHR score can improve CAD prediction and reclassification 1 year before diagnosis, beyond conventional clinical guidelines as determined by the pooled cohort equations (PCE) and a polygenic risk score for CAD.MethodsWe applied a machine learning framework using clinical features from the EHR in a multiethnic, clinical care cohort (BioMe) comprising 555 CAD cases and 6,349 control subjects and in a population-based cohort (UK Biobank) comprising 3,130 CAD cases and 378,344 control subjects for external validation.ResultsCompared with the PCE, the EHR score improved CAD prediction by 12% in the BioMe Biobank and by 9% in the UK Biobank. The EHR score reclassified 25.8% and 15.2% individuals in each cohort respectively, compared with the PCE score. We observed larger improvements in the EHR score over the PCE in a subgroup of individuals with low CAD risk, with 20% increased discrimination and 34.4% increased reclassification. In all models, the polygenic risk score for CAD did not improve CAD prediction, compared with the PCE or EHR score.ConclusionsThe EHR score resulted in increased prediction and reclassification for CAD, demonstrating its potential use for population health monitoring of short-term CAD risk in large health systems.
| S-EPMC8956801 | biostudies-literature
Project description:This article presents semiparametric joint models to analyze longitudinal data with recurrent event (e.g. multiple tumors, repeated hospital admissions) and terminal event such as death. A broad class of transformation models for the cumulative intensity of the recurrent events and the cumulative hazard of the terminal event is considered, which includes the proportional hazards model and the proportional odds model as special cases. We propose to estimate all the parameters using the nonparametric maximum likelihood estimators (NPMLE). We provide the simple and efficient EM algorithms to implement the proposed inference procedure. Asymptotic properties of the estimators are shown to be asymptotically normal and semiparametrically efficient. Finally, we evaluate the performance of the method through extensive simulation studies and a real-data application.
| S-EPMC3516390 | biostudies-literature
Project description:Columbia Open Health Data (COHD) is a publicly accessible database of electronic health record (EHR) prevalence and co-occurrence frequencies between conditions, drugs, procedures, and demographics. COHD was derived from Columbia University Irving Medical Center's Observational Health Data Sciences and Informatics (OHDSI) database. The lifetime dataset, derived from all records, contains 36,578 single concepts (11,952 conditions, 12,334 drugs, and 10,816 procedures) and 32,788,901 concept pairs from 5,364,781 patients. The 5-year dataset, derived from records from 2013-2017, contains 29,964 single concepts (10,159 conditions, 10,264 drugs, and 8,270 procedures) and 15,927,195 concept pairs from 1,790,431 patients. Exclusion of rare concepts (count ≤ 10) and Poisson randomization enable data sharing by eliminating risks to patient privacy. EHR prevalences are informative of healthcare consumption rates. Analysis of co-occurrence frequencies via relative frequency analysis and observed-expected frequency ratio are informative of associations between clinical concepts, useful for biomedical research tasks such as drug repurposing and pharmacovigilance. COHD is publicly accessible through a web application-programming interface (API) and downloadable from the Figshare repository. The code is available on GitHub.
| S-EPMC6257042 | biostudies-literature
Project description:Importance:Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of effort needed to use current methods might be mitigated by using routinely registered clinical notes. Objective:To develop and validate a multivariable prediction model for assessing inpatient violence risk based on machine learning techniques applied to clinical notes written in patients' electronic health records. Design, Setting, and Participants:This prognostic study used retrospective clinical notes registered in electronic health records during admission at 2 independent psychiatric health care institutions in the Netherlands. No exclusion criteria for individual patients were defined. At site 1, all adults admitted between January 2013 and August 2018 were included, and at site 2 all adults admitted to general psychiatric wards between June 2016 and August 2018 were included. Data were analyzed between September 2018 and February 2019. Main Outcomes and Measures:Predictive validity and generalizability of prognostic models measured using area under the curve (AUC). Results:Clinical notes recorded during a total of 3189 admissions of 2209 unique individuals at site 1 (mean [SD] age, 34.0 [16.6] years; 1536 [48.2%] male) and 3253 admissions of 1919 unique individuals at site 2 (mean [SD] age, 45.9 [16.6] years; 2097 [64.5%] male) were analyzed. Violent outcome was determined using the Staff Observation Aggression Scale-Revised. Nested cross-validation was used to train and evaluate models that assess violence risk during the first 4 weeks of admission based on clinical notes available after 24 hours. The predictive validity of models was measured at site 1 (AUC = 0.797; 95% CI, 0.771-0.822) and site 2 (AUC = 0.764; 95% CI, 0.732-0.797). The validation of pretrained models in the other site resulted in AUCs of 0.722 (95% CI, 0.690-0.753) at site 1 and 0.643 (95% CI, 0.610-0.675) at site 2; the difference in AUCs between the internally trained model and the model trained on other-site data was significant at site 1 (AUC difference = 0.075; 95% CI, 0.045-0.105; P < .001) and site 2 (AUC difference = 0.121; 95% CI, 0.085-0.156; P < .001). Conclusions and Relevance:Internally validated predictions resulted in AUC values with good predictive validity, suggesting that automatic violence risk assessment using routinely registered clinical notes is possible. The validation of trained models using data from other sites corroborates previous findings that violence risk assessment generalizes modestly to different populations.
| S-EPMC6613290 | biostudies-literature
Project description:ObjectivesThe development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames.Materials and methodsFIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models.ResultsUsing FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case.ConclusionFIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process.
| S-EPMC8327378 | biostudies-literature
Project description:OBJECTIVE:The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit. METHOD:Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%. RESULTS:Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively. CONCLUSIONS:Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.
| S-EPMC6167136 | biostudies-literature
Project description:The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10-15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; "middleware" products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
| S-EPMC5081968 | biostudies-literature
Project description:The aim of this study is to determine the most informative pre- and in-cycle variables for predicting success for a first autologous oocyte in-vitro fertilization (IVF) cycle. This is a retrospective study using 22,413 first autologous oocyte IVF cycles from 2001 to 2018. Models were developed to predict pregnancy following an IVF cycle with a fresh embryo transfer. The importance of each variable was determined by its coefficient in a logistic regression model and the prediction accuracy based on different variable sets was reported. The area under the receiver operating characteristic curve (AUC) on a validation patient cohort was the metric for prediction accuracy. Three factors were found to be of importance when predicting IVF success: age in three groups (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos. For predicting first-cycle IVF pregnancy using all available variables, the predictive model achieved an AUC of 68% + /- 0.01%. A parsimonious predictive model utilizing age (38-40, 41-42, and above 42 years old), number of transferred embryos, and number of cryopreserved embryos achieved an AUC of 65% + /- 0.01%. The proposed models accurately predict a single IVF cycle pregnancy outcome and identify important predictive variables associated with the outcome. These models are limited to predicting pregnancy immediately after the IVF cycle and not live birth. These models do not include indicators of multiple gestation and are not intended for clinical application.
| S-EPMC8763861 | biostudies-literature