Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans.
Ontology highlight
ABSTRACT:
SUBMITTER: Harrington KM
PROVIDER: S-EPMC6699164 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:STUDY OBJECTIVES:We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States. METHODS:Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ? 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated. RESULTS:The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ? 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ? 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases. CONCLUSIONS:A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.
| S-EPMC7053021 | biostudies-literature
Project description:BackgroundA lack of studies with large sample sizes of patients with rotator cuff tears is a barrier to performing clinical and genomic research.ObjectiveTo develop and validate an electronic medical record (EMR)-based algorithm to identify individuals with and without rotator cuff tear.DesignWe used a deidentified version of the EMR of more than 2 million subjects. A screening algorithm was applied to classify subjects into likely rotator cuff tear and likely normal rotator cuff groups. From these subjects, 500 likely rotator cuff tear and 500 likely normal rotator cuff were randomly chosen for algorithm development. Chart review of all 1000 subjects confirmed the true phenotype of rotator cuff tear or normal rotator cuff based on magnetic resonance imaging and operative report. An algorithm was then developed based on logistic regression and validation of the algorithm was performed.ResultsThe variables significantly predicting rotator cuff tear included the number of times a Current Procedural Terminology code related to rotator cuff procedures was used (odds ratio [OR] = 3.3; 95% confidence interval [CI]: 1.6-6.8 for ≥3 vs 0), the number of times a term related to rotator cuff lesions occurred in radiology reports (OR = 2.2; 95% CI: 1.2-4.1 for ≥1 vs 0), and the number of times a term related to rotator cuff lesions occurred in physician notes (OR = 4.5; 95% CI: 2.2-9.1 for 1 or 2 times vs 0). This phenotyping algorithm had a specificity of 0.89 (95% CI: 0.79-0.95) for rotator cuff tear, area under the curve (AUC) of 0.842, and diagnostic likelihood ratios (DLRs), DLR+ and DLR- of 5.94 (95% CI: 3.07-11.48) and 0.363 (95% CI: 0.291-0.453).ConclusionOur informatics algorithm enables identification of cohorts of individuals with and without rotator cuff tear from an EMR-based data set with moderate accuracy.
| S-EPMC7593991 | biostudies-literature
Project description:Background and aimsNonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study sought to design a classification algorithm for NAFLD within the electronic medical record (EMR) for the development of large-scale longitudinal cohorts.MethodsWe implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P < 0.05 were included in the multivariable analysis.ResultsThe NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD, and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with AUC of 0.85 versus 0.75 (P < 0.0001) and leads to the creation of a new independent cohort of 8458 individuals with a high probability for NAFLD.ConclusionsThe NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy, and can be applied across different institutions to create EMR-based cohorts of individuals with NAFLD.
| S-EPMC4761309 | biostudies-literature
Project description:ObjectiveThe goal of this investigation was to determine whether automated approaches can learn patient-oriented care teams via utilization of an electronic medical record (EMR) system.Materials and methodsTo perform this investigation, we designed a data-mining framework that relies on a combination of latent topic modeling and network analysis to infer patterns of collaborative teams. We applied the framework to the EMR utilization records of over 10 000 employees and 17 000 inpatients at a large academic medical center during a 4-month window in 2010. Next, we conducted an extrinsic evaluation of the patterns to determine the plausibility of the inferred care teams via surveys with knowledgeable experts. Finally, we conducted an intrinsic evaluation to contextualize each team in terms of collaboration strength (via a cluster coefficient) and clinical credibility (via associations between teams and patient comorbidities).ResultsThe framework discovered 34 collaborative care teams, 27 (79.4%) of which were confirmed as administratively plausible. Of those, 26 teams depicted strong collaborations, with a cluster coefficient > 0.5. There were 119 diagnostic conditions associated with 34 care teams. Additionally, to provide clarity on how the survey respondents arrived at their determinations, we worked with several oncologists to develop an illustrative example of how a certain team functions in cancer care.DiscussionInferred collaborative teams are plausible; translating such patterns into optimized collaborative care will require administrative review and integration with management practices.ConclusionsEMR utilization records can be mined for collaborative care patterns in large complex medical centers.
| S-EPMC6080725 | biostudies-literature
Project description:Posttraumatic stress disorder (PTSD) is a chronic and disabling psychiatric disorder prevalent in military veterans. Epigenetic mechanisms have been implicated in the etiology of PTSD, with DNA methylation being the most studied to identify novel molecular biomarkers associated with this disorder. We performed one of the largest single-sample epigenome-wide association studies (EWAS) of PTSD to date. Our sample included 1135 male European-American U.S. veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). DNA was collected from saliva samples and the Illumina HumanMethylation EPIC BeadChip was used for the methylation analysis. PTSD was assessed using the PTSD Checklist. An EWAS was conducted using linear regression adjusted for age, cell-type proportions, first 10 principal components, and smoking status. After Bonferroni correction, we identified six genome-wide significant (GWS) CpG sites associated with past-month PTSD and three CpGs with lifetime PTSD (prange = 10-10-10-8). These CpG sites map to genes involved in immune function, transcription regulation, axonal guidance, cell signaling, and protein binding. Among these, SENP7, which is involved in transcription regulation and has been linked to risk-taking behavior and alcohol consumption in genome-wide association studies, replicated in an independent veteran cohort and was downregulated in medial orbitofrontal cortex of PTSD postmortem brain tissue. These findings suggest potential epigenetic biomarkers of PTSD that may help inform the pathophysiology of this disorder in veterans and other trauma-affected populations.
| S-EPMC8854688 | biostudies-literature
Project description:ObjectiveCohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.MethodsWe extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH) (N = 150) and Cincinnati Children's Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.ResultsThe rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters.ConclusionsIn a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.
| S-EPMC4966969 | biostudies-literature
Project description:Recent work inspired by graph theory has begun to conceptualize mental disorders as networks of interacting symptoms. Posttraumatic stress disorder (PTSD) symptom networks have been investigated in clinical samples meeting full diagnostic criteria, including military veterans, natural disaster survivors, civilian survivors of war, and child sexual abuse survivors. Despite reliable associations across reported networks, more work is needed to compare central symptoms across trauma types. Additionally, individuals without a diagnosis who still experience symptoms, also referred to as subthreshold cases, have not been explored with network analysis in veterans. A sample of 1,050 Iraq/Afghanistan-era U.S. military veterans (851 males, mean age = 36.3, SD = 9.53) meeting current full-criteria PTSD (n = 912) and subthreshold PTSD (n = 138) were assessed with the Structured Clinical Interview for DSM-IV Disorders (SCID). Combat Exposure Scale (CES) scores were used to group the sample meeting full-criteria into high (n = 639) and low (n = 273) combat exposure subgroups. Networks were estimated using regularized partial correlation models in the R-package qgraph, and robustness tests were performed with bootnet. Frequently co-occurring symptom pairs (strong network connections) emerged between two avoidance symptoms, hypervigilance and startle response, loss of interest and detachment, as well as, detachment and restricted affect. These associations replicate findings reported across PTSD trauma types. A symptom network analysis of PTSD in a veteran population found significantly greater overall connectivity in the full-criteria PTSD group as compared to the subthreshold PTSD group. Additionally, novel findings indicate that the association between intrusive thoughts and irritability is a feature of the symptom network of veterans with high levels of combat exposure. Mean node predictability is high for PTSD symptom networks, averaging 51.5% shared variance. With the tools described here and by others, researchers can help refine diagnostic criteria for PTSD, develop more accurate measures for assessing PTSD, and eventually inform therapies that target symptoms with strong network connections to interrupt interconnected symptom complexes and promote functional recovery.
| S-EPMC6259007 | biostudies-literature
Project description:BackgroundDeploying accurate computable phenotypes in pragmatic trials requires a trade-off between precise and clinically sensical variable selection. In particular, evaluating the medical encounter to assess a pattern leading to clinically significant impairment or distress indicative of disease is a difficult modeling challenge for the emergency department.ObjectiveThis study aimed to derive and validate an electronic health record-based computable phenotype to identify emergency department patients with opioid use disorder using physician chart review as a reference standard.MethodsA two-algorithm computable phenotype was developed and evaluated using structured clinical data across 13 emergency departments in two large health care systems. Algorithm 1 combined clinician and billing codes. Algorithm 2 used chief complaint structured data suggestive of opioid use disorder. To evaluate the algorithms in both internal and external validation phases, two emergency medicine physicians, with a third acting as adjudicator, reviewed a pragmatic sample of 231 charts: 125 internal validation (75 positive and 50 negative), 106 external validation (56 positive and 50 negative).ResultsCohen kappa, measuring agreement between reviewers, for the internal and external validation cohorts was 0.95 and 0.93, respectively. In the internal validation phase, Algorithm 1 had a positive predictive value (PPV) of 0.96 (95% CI 0.863-0.995) and a negative predictive value (NPV) of 0.98 (95% CI 0.893-0.999), and Algorithm 2 had a PPV of 0.8 (95% CI 0.593-0.932) and an NPV of 1.0 (one-sided 97.5% CI 0.863-1). In the external validation phase, the phenotype had a PPV of 0.95 (95% CI 0.851-0.989) and an NPV of 0.92 (95% CI 0.807-0.978).ConclusionsThis phenotype detected emergency department patients with opioid use disorder with high predictive values and reliability. Its algorithms were transportable across health care systems and have potential value for both clinical and research purposes.
| S-EPMC6913746 | biostudies-literature
Project description:BackgroundStudy of pulmonary arterial hypertension (PAH) in claims-based (CB) cohorts may facilitate understanding of disease epidemiology, however previous CB algorithms to identify PAH have had limited test characteristics. We hypothesized that machine learning algorithms (MLA) could accurately identify PAH in an CB cohort.MethodsICD-9/10 codes, CPT codes or PAH medications were used to screen an electronic medical record (EMR) for possible PAH. A subset (Development Cohort) was manually reviewed and adjudicated as PAH or "not PAH" and used to train and test MLAs. A second subset (Refinement Cohort) was manually reviewed and combined with the Development Cohort to make The Final Cohort, again divided into training and testing sets, with MLA characteristics defined on test set. The MLA was validated using an independent EMR cohort.Results194 PAH and 786 "not PAH" in the Development Cohort trained and tested the initial MLA. In the Final Cohort test set, the final MLA sensitivity was 0.88, specificity was 0.93, positive predictive value was 0.89, and negative predictive value was 0.92. Persistence and strength of PAH medication use and CPT code for right heart catheterization were principal MLA features. Applying the MLA to the EMR cohort using a split cohort internal validation approach, we found 265 additional non-confirmed cases of suspected PAH that exhibited typical PAH demographics, comorbidities, hemodynamics.ConclusionsWe developed and validated a MLA using only CB features that identified PAH in the EMR with strong test characteristics. When deployed across an entire EMR, the MLA identified cases with known features of PAH.
| S-EPMC9145474 | biostudies-literature
Project description:Objective: Posttraumatic stress disorder (PTSD) affects a high proportion of returning combat veterans, but the biological mechanisms of PTSD remain unclear. Circulating micro RNAs (miRNAs) have been associated with depression, and anxiety disorders, but there is little understanding of how miRNAs may relate to PTSD. In this study we compare profiles of circulating miRNA in combat veterans with and without PTSD in order to better understand biological mechanisms of PTSD. Methods: Blood from 24 male military service members was collected following deployment to Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF), and subjects were assessed for PTSD symptoms using the PTSD checklist-military version. miRNA was isolated from whole blood and sequenced on the Ion Torrent PGM™ using the Ion 316 Chip v2. Differences in miRNA expression was compared between subjects with PTSD (N=15) and combat matched controls without PTSD (N=9). Significantly different miRNA, according to a FDR≤0.05, were assessed for predictive putative targets, and pathway analysis of related targets was completed. Results: PTSD was associated with 4 upregulated and 4 downregulated miRNA, including a 2.94 fold increase in miR-19a-3p and a 1.56 fold decrease in miR-15b. Pathway analysis show that PTSD is related to the axon guidance and Wnt signaling pathways, which work together along with the adherens junction and MAPK signaling pathways to support neuronal development through regulation of growth cones. The PTSD associated miRNAs related to transcription factors, including Transcription factor 7 (T-cell specific, HMG-box), Transcription factor 7 like 1, and Transcription factor 7 like 2. Conclusions: PTSD is associated with miRNAs that regulate biological functions that include neuronal activities, suggesting that they play a role in PTSD symptomatology.
2017-03-07 | GSE87768 | GEO