Abstract only Introduction: In-hospital cardiac arrest (IHCA) is experienced by approximately 200,000 patients annually in the US. While individual care teams can readily identify IHCA at the ...bedside, subsequent event identification for research or quality improvement (QI) purposes is challenging and often relies on the use of administrative billing codes. Prior research has shown that the use of administrative codes from International Classification of Diseases-9 (ICD-9) was both insensitive and nonspecific for identification of IHCA events. However, the performance of this approach using updated ICD-10 codes has not been established. Hypothesis: ICD-10 codes can be used to identify IHCAs in a QI database of IHCA events with high sensitivity. Methods: We performed a retrospective cohort study of all adult IHCA patients ( > 18 years old) at the Hospital of the University of Pennsylvania from 06/2018-03/2022. True IHCA events were confirmed by a research coordinator and a physician through detailed encounter-level chart review of clinical records. We searched for four individual ICD-10 diagnostic codes: ICD I46.2 - cardiac arrest due to underlying cardiac condition; ICD I46.8 - cardiac arrest due to other underlying condition; ICD I46.9 - cardiac arrest, unspecified; and ICD I49.01 - ventricular fibrillation. ICD-identified IHCA was defined as the presence of any of these codes from a hospital stay but not present on hospital arrival. Results: Of 466 patients with a confirmed IHCA event contained within the QI database, the median age was 65 (54-73), 43% (199/466) were female, and 69% (323/466) had sustained return of spontaneous circulation (ROSC) at the end of their code. The use of billing codes identified 318 patients as having IHCA, corresponding to a sensitivity of 68%. The most used ICD-10 code in this cohort was ICD I46.9 (141/466, 30%). Conclusions: The use of ICD-10 codes has a low sensitivity for identification of IHCA events. These findings are consistent with previously published work using older ICD iterations and suggests significant limitations with using administrative codes to identify IHCA events. Novel approaches (e.g., natural language processing and machine learning algorithms) to identify IHCA may facilitate more accurate research and QI efforts.
Accurate temporal identification and normalization is imperative for many biomedical and clinical tasks such as generating timelines and identifying phenotypes. A major natural language processing ...challenge is developing and evaluating a generalizable temporal modeling approach that performs well across corpora and institutions. Our long-term goal is to create such a model. We initiate our work on reaching this goal by focusing on temporal expression (TIMEX3) identification. We present a systematic approach to 1) generalize existing solutions for automated TIMEX3 span detection, and 2) assess similarities and differences by various instantiations of TIMEX3 models applied on separate clinical corpora. When evaluated on the 2012 i2b2 and the 2015 Clinical TempEval challenge corpora, our conclusion is that our approach is successful - we achieve competitive results for automated classification, and we identify similarities and differences in TIMEX3 modeling that will be informative in the development of a simplified, general temporal model.
The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In ...clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System. This reference standard can be used to improve patient understanding by linking to web sources with lay descriptions of annotated short forms or by substituting short forms with a more simplified, lay term.
In this study, we evaluate 1) accuracy of participating systems' normalizing short forms compared to a majority sense baseline approach, 2) performance of participants' systems for short forms with variable majority sense distributions, and 3) report the accuracy of participating systems' normalizing shared normalized concepts between the test set and the Consumer Health Vocabulary, a vocabulary of lay medical terms.
The best systems submitted by the five participating teams performed with accuracies ranging from 43 to 72 %. A majority sense baseline approach achieved the second best performance. The performance of participating systems for normalizing short forms with two or more senses with low ambiguity (majority sense greater than 80 %) ranged from 52 to 78 % accuracy, with two or more senses with moderate ambiguity (majority sense between 50 and 80 %) ranged from 23 to 57 % accuracy, and with two or more senses with high ambiguity (majority sense less than 50 %) ranged from 2 to 45 % accuracy. With respect to the ShARe test set, 69 % of short form annotations contained common concept unique identifiers with the Consumer Health Vocabulary. For these 2594 possible annotations, the performance of participating systems ranged from 50 to 75 % accuracy.
Short form normalization continues to be a challenging problem. Short form normalization systems perform with moderate to reasonable accuracies. The Consumer Health Vocabulary could enrich its knowledge base with missed concept unique identifiers from the ShARe test set to further support patient understanding of unfamiliar medical terms.
. Family health history (FHH) can be used to identify individuals at elevated risk for familial cancers. Risk criteria for common cancers rely on age of onset, which is documented inconsistently as ...structured and unstructured data in electronic health records (EHRs).
. To investigate a natural language processing (NLP) approach to extract age of onset and age of death from free-text EHR fields.
. Using 474,651 FHH entries from 89,814 patients, we investigated two methods - frequent patterns (baseline) and NLP classifier.
. For age of onset, the NLP classifier outperformed the baseline in precision (96% vs. 83%; 95% CI 94, 97 and 80, 86) with equivalent recall (both 93%; 95% CI 91, 95). When applied to the full dataset, the NLP approach increased the percentage of FHH entries for which cancer risk criteria could be applied from 10% to 15%.
. NLP combined with structured data may improve the computation of familial cancer risk criteria for various use cases.
We present a pilot study of an annotation schema representing problems and their attributes, along with their relationship to temporal modifiers. We evaluated the ability for humans to annotate ...clinical reports using the schema and assessed the contribution of semantic annotations in determining the status of a problem mention as active, inactive, proposed, resolved, negated, or other. Our hypothesis is that the schema captures semantic information useful for generating an accurate problem list. Clinical named entities such as reference events, time points, time durations, aspectual phase, ordering words and their relationships including modifications and ordering relations can be annotated by humans with low to moderate recall. Once identified, most attributes can be annotated with low to moderate agreement. Some attributes - Experiencer, Existence, and Certainty - are more informative than other attributes - Intermittency and Generalized/Conditional - for predicting a problem mention's status. Support vector machine outperformed Naïve Bayes and Decision Tree for predicting a problem's status.
Objectives: We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis.
Methods: We conducted ...a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers.
Results: Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications.
Conclusions: There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.
We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed ...an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
ObjectiveTo assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June ...2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic.Design, setting and participantsThis is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic.Primary and secondary outcome measuresThe primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation.ResultsBaseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was –4.72 mg/dL vs –4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March–April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March–April 2020 vs 14.3% in November 2020 to January 2021, p<0.001).ConclusionsAdmission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.
In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We ...evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.
A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.
Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).
Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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