Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 48
Filtrar
Filtros adicionais











País/Região como assunto
Intervalo de ano
1.
Artigo em Inglês | MEDLINE | ID: mdl-31390016

RESUMO

OBJECTIVE: Clinical corpora can be deidentified using a combination of machine-learned automated taggers and hiding in plain sight (HIPS) resynthesis. The latter replaces detected personally identifiable information (PII) with random surrogates, allowing leaked PII to blend in or "hide in plain sight." We evaluated the extent to which a malicious attacker could expose leaked PII in such a corpus. MATERIALS AND METHODS: We modeled a scenario where an institution (the defender) externally shared an 800-note corpus of actual outpatient clinical encounter notes from a large, integrated health care delivery system in Washington State. These notes were deidentified by a machine-learned PII tagger and HIPS resynthesis. A malicious attacker obtained and performed a parrot attack intending to expose leaked PII in this corpus. Specifically, the attacker mimicked the defender's process by manually annotating all PII-like content in half of the released corpus, training a PII tagger on these data, and using the trained model to tag the remaining encounter notes. The attacker hypothesized that untagged identifiers would be leaked PII, discoverable by manual review. We evaluated the attacker's success using measures of leak-detection rate and accuracy. RESULTS: The attacker correctly hypothesized that 211 (68%) of 310 actual PII leaks in the corpus were leaks, and wrongly hypothesized that 191 resynthesized PII instances were also leaks. One-third of actual leaks remained undetected. DISCUSSION AND CONCLUSION: A malicious parrot attack to reveal leaked PII in clinical text deidentified by machine-learned HIPS resynthesis can attenuate but not eliminate the protective effect of HIPS deidentification.

2.
Am J Hum Genet ; 2019 Aug 08.
Artigo em Inglês | MEDLINE | ID: mdl-31422818

RESUMO

As clinical testing for Mendelian causes of colorectal cancer (CRC) is largely driven by recognition of family history and early age of onset, the rates of such findings among individuals with prevalent CRC not recognized to have these features is largely unknown. We evaluated actionable genomic findings in community-based participants ascertained by three phenotypes: (1) CRC, (2) one or more adenomatous colon polyps, and (3) control participants over age 59 years without CRC or colon polyps. These participants underwent sequencing for a panel of genes that included colorectal cancer/polyp (CRC/P)-associated and actionable incidental findings genes. Those with CRC had a 3.8% rate of positive results (pathogenic or likely pathogenic) for a CRC-associated gene variant, despite generally being older at CRC onset (mean 72 years). Those ascertained for polyps had a 0.8% positive rate and those with no CRC/P had a positive rate of 0.2%. Though incidental finding rates unrelated to colon cancer were similar for all groups, our positive rate for cardiovascular findings exceeds disease prevalence, suggesting that variant interpretation challenges or low penetrance in these genes. The rate of HFE c.845G>A (p.Cys282Tyr) homozygotes in the CRC group reinforces a previously reported, but relatively unexplored, association between hemochromatosis and CRC. These results in a general clinical population suggest that current testing strategies could be improved in order to better detect Mendelian CRC-associated conditions. These data also underscore the need for additional functional and familial evidence to clarify the pathogenicity and penetrance of variants deemed pathogenic or likely pathogenic, particularly among the actionable genes associated with cardiovascular disease.

3.
J Biomed Inform ; 96: 103253, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31325501

RESUMO

BACKGROUND: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. METHODS: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. RESULTS: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. CONCLUSION: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

4.
BMC Med ; 17(1): 135, 2019 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-31311600

RESUMO

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. METHODS: First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). RESULTS: Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10- 20). This effect was consistent in both pediatric (p = 9.92 × 10- 6) and adult (p = 9.73 × 10- 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10- 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10- 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10- 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10- 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. CONCLUSIONS: In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

5.
Pharmacoepidemiol Drug Saf ; 28(8): 1143-1151, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31218780

RESUMO

PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases. METHODS: We developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self-harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart-reviewed to provide a gold standard for comparison and evaluation of the automated method. RESULTS: The method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid-related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database. CONCLUSIONS: Methods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.

6.
Pharmacoepidemiol Drug Saf ; 28(8): 1127-1137, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31020755

RESUMO

PURPOSE: The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS: Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS: Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS: Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

7.
Genet Med ; 21(9): 2135-2144, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30890783

RESUMO

PURPOSE: To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs. METHODS: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap). RESULTS: Thirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation, and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multiexon and 29 single-exon CNVs with high C-scores were assessed by Multiplex Ligation-dependent Probe Amplification (MLPA). CONCLUSION: Atlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We propose guidelines and criteria to identify high confidence single-exon CNVs.

8.
J Am Med Inform Assoc ; 26(3): 219-227, 2019 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-30590688

RESUMO

Objective: We describe a stratified sampling design that combines electronic health records (EHRs) and United States Census (USC) data to construct the sampling frame and an algorithm to enrich the sample with individuals belonging to rarer strata. Materials and Methods: This design was developed for a multi-site survey that sought to examine patient concerns about and barriers to participating in research studies, especially among under-studied populations (eg, minorities, low educational attainment). We defined sampling strata by cross-tabulating several socio-demographic variables obtained from EHR and augmented with census-block-level USC data. We oversampled rarer and historically underrepresented subpopulations. Results: The sampling strategy, which included USC-supplemented EHR data, led to a far more diverse sample than would have been expected under random sampling (eg, 3-, 8-, 7-, and 12-fold increase in African Americans, Asians, Hispanics and those with less than a high school degree, respectively). We observed that our EHR data tended to misclassify minority races more often than majority races, and that non-majority races, Latino ethnicity, younger adult age, lower education, and urban/suburban living were each associated with lower response rates to the mailed surveys. Discussion: We observed substantial enrichment from rarer subpopulations. The magnitude of the enrichment depends on the accuracy of the variables that define the sampling strata and the overall response rate. Conclusion: EHR and USC data may be used to define sampling strata that in turn may be used to enrich the final study sample. This design may be of particular interest for studies of rarer and understudied populations.

9.
Circulation ; 138(22): 2469-2481, 2018 Nov 27.
Artigo em Inglês | MEDLINE | ID: mdl-30571344

RESUMO

BACKGROUND: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. METHODS: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). RESULTS: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-ß predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-ß. CONCLUSIONS: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

10.
Genes Immun ; 2018 Nov 21.
Artigo em Inglês | MEDLINE | ID: mdl-30459343

RESUMO

Resting-state white blood cell (WBC) count is a marker of inflammation and immune system health. There is evidence that WBC count is not fixed over time and there is heterogeneity in WBC trajectory that is associated with morbidity and mortality. Latent class mixed modeling (LCMM) is a method that can identify unobserved heterogeneity in longitudinal data and attempts to classify individuals into groups based on a linear model of repeated measurements. We applied LCMM to repeated WBC count measures derived from electronic medical records of participants of the National Human Genetics Research Institute (NHRGI) electronic MEdical Record and GEnomics (eMERGE) network study, revealing two WBC count trajectory phenotypes. Advancing these phenotypes to GWAS, we found genetic associations between trajectory class membership and regions on chromosome 1p34.3 and chromosome 11q13.4. The chromosome 1 region contains CSF3R, which encodes the granulocyte colony-stimulating factor receptor. This protein is a major factor in neutrophil stimulation and proliferation. The association on chromosome 11 contain genes RNF169 and XRRA1; both involved in the regulation of double-strand break DNA repair.

11.
Artigo em Inglês | MEDLINE | ID: mdl-30326300

RESUMO

BACKGROUND & AIMS: There is significant variation among endoscopists in their adenoma detection rates (ADRs). We explored associations between ADR and characteristics of endoscopists, including personality traits and financial incentives. METHODS: We collected electronic health record data from October 2013 through September 2015 and calculated ADRs for physicians from 4 health systems. ADRs were risk-adjusted for differences in patient populations. Physicians were surveyed to assess financial motivations, knowledge and perceptions about colonoscopy quality, and personality traits. Of 140 physicians sent the survey, 117 responded. RESULTS: The median risk-adjusted ADR for all surveyed physicians was 29.3% (interquartile range, 24.1%-35.5%). We found no significant association between ADR and financial incentives, malpractice concerns, or physicians' perceptions of ADR as a quality metric. ADR was associated with the degree of self-reported compulsiveness relative to peers: among endoscopists who described themselves as much more compulsive, the ADR was 33.1%; among those who described themselves as somewhat more compulsive, the ADR was 32.9%; among those who described themselves as about the same as others, the ADR was 26.4%; and among those who described themselves as somewhat less compulsive, the ADR was 27.3%) (P = .0019). ADR also associated with perceived thoroughness (much more thorough than peers, ADR = 31.5%; somewhat more, 31.9%; same/somewhat less, 27.1%; P = .0173). Physicians who reported feeling rushed, having difficulty pacing themselves, or having difficulty in accomplishing goals had higher ADRs. A secondary analysis found the same associations between personality and adenomas per colonoscopy. CONCLUSIONS: In a survey of endoscopists and comparison of results with ADRs, we found no significant association between ADR and financial incentives, malpractice concerns, or perceptions of ADR as a quality metric. However, ADRs were higher among physicians who described themselves as more compulsive or thorough, and among those who reported feeling rushed or having difficulty accomplishing goals.

12.
Nat Commun ; 9(1): 3522, 2018 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-30166544

RESUMO

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.

14.
Endoscopy ; 50(10): 984-992, 2018 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-29689571

RESUMO

BACKGROUND: Serrated polyps are important colorectal cancer precursors that are variably detected during colonoscopy. We measured serrated polyp detection rate (SPDR) in a large, multicenter, cross-sectional study of colonoscopy quality to identify drivers of SPDR variation. METHODS: Colonoscopy and pathology reports were collected for a 2-year period (10/2013-9/2015) from four sites across the United States. Data from reports, including size, location, and histology of polyps, were abstracted using a validated natural language processing algorithm. SPDR was defined as the proportion of colonoscopies with ≥ 1 serrated polyp (not including hyperplastic polyps). Multivariable logistic regression was performed to determine endoscopist characteristics associated with serrated polyp detection. RESULTS: A total of 104 618 colonoscopies were performed by 201 endoscopists who varied with respect to specialty (86 % were gastroenterologists), sex (18 % female), years in practice (range 1 - 51), and number of colonoscopies performed during the study period (range 30 - 2654). The overall mean SPDR was 5.1 % (SD 3.8 %, range 0 - 18.8 %). In multivariable analysis, gastroenterology specialty training (odds ratio [OR] 1.89, 95 % confidence interval [CI] 1.33 - 2.70), fewer years in practice (≤ 9 years vs. ≥ 27 years: OR 1.52, 95 %CI 1.14 - 2.04)], and higher procedure volumes (highest vs. lowest quartile: OR 1.77, 95 %CI 1.27 - 2.46)] were independently associated with serrated polyp detection. CONCLUSIONS: Gastroenterology specialization, more recent completion of training, and greater procedure volume are associated with serrated polyp detection. These findings imply that both repetition and training are likely to be important contributors to adequate detection of these important cancer precursors. Additional efforts to improve SPDR are needed.

15.
Acad Radiol ; 25(11): 1422-1432, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29605561

RESUMO

RATIONALE AND OBJECTIVES: To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. MATERIALS AND METHODS: We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). CONCLUSIONS: Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC.

16.
Am J Psychiatry ; 175(5): 434-442, 2018 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-29361848

RESUMO

OBJECTIVE: The authors sought to describe patterns of health care use prior to first diagnosis of a psychotic disorder in a population-based sample. METHOD: Electronic health records and insurance claims from five large integrated health systems were used to identify 624 patients 15-29 years old who received a first diagnosis of a psychotic disorder in any care setting and to record health services received, diagnoses assigned, and medications dispensed during the previous 36 months. Patterns of utilization were compared between patients receiving a first diagnosis of a psychotic disorder and matched samples of general health system members and members receiving a first diagnosis of unipolar depression. RESULTS: During the year before a first psychotic disorder diagnosis, 29% of patients had mental health specialty outpatient care, 8% had mental health inpatient care, 24% had emergency department mental health care, 29% made a primary care visit with a mental health diagnosis, and 60% received at least one mental health diagnosis (including substance use disorders). Compared with patients receiving a first diagnosis of unipolar depression, those with a first diagnosis of a psychotic disorder were modestly more likely to use all types of health services and were specifically more likely to use mental health inpatient care (odds ratio=2.96, 95% CI=1.97-4.43) and mental health emergency department care (rate ratio=3.74, 95% CI=3.39-4.53). CONCLUSIONS: Most patients receiving a first diagnosis of a psychotic disorder had some indication of mental health care need during the previous year. General use of primary care or mental health services, however, does not clearly distinguish people who later receive a diagnosis of a psychotic disorder from those who later receive a diagnosis of unipolar depression. Use of inpatient or emergency department mental health care is a more specific indicator of risk.

17.
Am J Gastroenterol ; 113(3): 431-439, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-29380819

RESUMO

OBJECTIVES: Endoscopist quality measures such as adenoma detection rate (ADR) and serrated polyp detection rates (SPDRs) depend on pathologist classification of histology. Although variation in pathologic interpretation is recognized, we add to the literature by quantifying the impact of pathologic variability on endoscopist performance. METHODS: We used natural language processing to abstract relevant data from colonoscopy and related pathology reports performed over 2 years at four clinical sites. We quantified each pathologist's likelihood of classifying polyp specimens as adenomas or serrated polyps. We estimated the impact on endoscopists' ADR and SPDR of sending their specimens to pathologists with higher or lower classification rates. RESULTS: We observed 85,526 colonoscopies performed by 119 endoscopists; 50,453 had a polyp specimen, which were analyzed by 48 pathologists. There was greater variation across pathologists in classification of serrated polyps than in classification of adenomas. We estimate the endoscopist's average SPDR would be 0.5% if all their specimens were analyzed by the pathologist in our sample with the lowest classification rate and 12.0% if all their specimens were analyzed by the pathologist with the highest classification rate. In contrast, the endoscopist's average ADR would be 28.5% and 42.4% if their specimens were analyzed by the pathologist with lowest and highest classification rate, respectively. CONCLUSIONS: There is significant variation in pathologic interpretation, which more substantially affects endoscopist SPDR than ADR.

18.
Gastrointest Endosc ; 87(3): 778-786.e5, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28866456

RESUMO

BACKGROUND AND AIMS: Patients who receive a colonoscopy from a physician with a low adenoma detection rate (ADR) are at higher risk of subsequent colorectal cancer. It is unclear what drives the variation across physicians in ADR. We describe physician characteristics associated with higher ADR. METHODS: In this retrospective cohort study a natural language processing system was used to analyze all outpatient colonoscopy examinations and their associated pathology reports from October 2013 to September 2015 for adults age 40 years and older across physicians from 4 diverse health systems. Physician performance on ADR was risk adjusted for differences in patient population and procedure indication. Our sample included 201 physicians performing at least 30 colonoscopy examinations during the study period, totaling 104,618 colonoscopy examinations. RESULTS: The mean ADR was 33.2% (range, 6.3%-58.7%). Higher ADR was seen among female physicians (4.2 percentage points higher than men, P = .020), gastroenterologists (9.4 percentage points higher than nongastroenterologists, P < .001), and physicians with ≤9 years since their residency completion (6.0 percentage points higher than physicians who have had 27-51 years of practice, P = .004). CONCLUSIONS: Gastroenterologists, female physicians, and more recently trained physicians had higher performance in adenoma detection.


Assuntos
Adenoma/diagnóstico , Competência Clínica/estatística & dados numéricos , Colonoscopia/estatística & dados numéricos , Neoplasias Colorretais/diagnóstico , Médicos/estatística & dados numéricos , Adenoma/patologia , Adulto , Idoso , Estudos de Coortes , Neoplasias Colorretais/patologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Processamento de Linguagem Natural , Sistema de Registros , Estudos Retrospectivos
19.
J Pain Palliat Care Pharmacother ; 32(2-3): 106-115, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30702378

RESUMO

Use of prescription opioids and problems of abuse and addiction have increased over the past decade. Claims-based studies have documented substantial economic burden of opioid abuse. This study utilized electronic health record (EHR) data to identify chronic opioid therapy (COT) patients with problem opioid use (POU) and compared costs with those for COT patients without POU. This study utilized EHR and claims data from an integrated health care system. Patients received COT (≥70 days' supply in ≥1 calendar quarter, 2006-2012). Natural language processing (NLP) identified notations of opioid addiction, abuse, misuse, or overuse, and manual validation was performed. Cases had evidence of POU (index = first POU notation), and controls, sampled 9:1, did not. Health care resource utilization was measured and costs estimated using Medicare reimbursement rates. A longitudinal analysis of costs was conducted using generalized estimating equations. Adjusted analyses controlled for baseline age, gender, region, specific comorbidities, and a comorbidity index. The analysis population included 1,125 cases and 10,128 controls. Unadjusted costs were higher for cases in all three years. After controlling for covariates, total costs remained higher in cases and were significantly higher in the first year of follow-up ($38,064 vs. $31,674, P = .0048). The largest cost difference was observed in the first month of follow-up. COT patients with POU experienced significantly higher costs compared with COT patients without POU in the first year of follow-up. The greatest difference in costs was observed around identification of POU.


Assuntos
Analgésicos Opioides/administração & dosagem , Efeitos Psicossociais da Doença , Processamento de Linguagem Natural , Transtornos Relacionados ao Uso de Opioides/epidemiologia , Adolescente , Adulto , Idoso , Analgésicos Opioides/economia , Estudos de Casos e Controles , Registros Eletrônicos de Saúde , Feminino , Seguimentos , Custos de Cuidados de Saúde , Humanos , Estudos Longitudinais , Masculino , Medicare/economia , Pessoa de Meia-Idade , Transtornos Relacionados ao Uso de Opioides/economia , Estudos Retrospectivos , Estados Unidos , Adulto Jovem
20.
BioData Min ; 10: 25, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28770004

RESUMO

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. CONCLUSIONS: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA