Dataset Information

Serious conditions among patients with non-specific chief complaints in the pre-hospital setting: a retrospective cohort study.

ABSTRACT: BACKGROUND:Emergency Medical Services (EMS) are faced daily with patients presenting with a non-specific chief complaints (NSC); i.e. decreased general condition, general malaise, sense of illness, or just being unable to cope with usual daily activities. Patients presenting with NSCs often have normal vital signs. It has previously been established that however, NSCs may have a serious underlying condition that has yet to be identified. The primary outcome of this study was to determine the prevalence of serious conditions in patients presenting with NSCs to the EMS. METHOD:A retrospective cohort study of patients ?18?years of age who were reported as presenting with chief complaints compatible with NSCs to the EMS in Stockholm Region and transported to an emergency department between January 1st, 2013 and December 31st, 2013. Patients were identified via the EMS electronic health care record and followed via records from the National Patient Registry and Causes of Death Registry at Sweden's National Board for Health and Welfare. The definition of serious condition was defined by expert consensus. Descriptive statistics as well as regression analyses were used. RESULTS:A total of 3780 patients were included, with a median age of 77?years. A serious condition was present in 35.3% of the patients. The in-hospital mortality rate for the group with serious conditions was 10.1% (OR 6.8, CI 95%, 4.1-11.3), and the 30-day mortality rate was 20.2% (OR 3.1, CI 95%, 2.3-4.0). In the group with no serious conditions the rates were 1.0 and 4.2%, respectively. The total hospitalization rate was 67.6%. The presence of serious conditions as well as increased mortality rates were associated with Rapid Emergency Triage and Treatment system (RETTS) as well as National Early Warning Score (NEWS) irrespective of triage score. CONCLUSION:More than one-third of the patients presenting with NSCs to EMS had a serious underlying condition which was associated with increased mortality and hospitalization rates. TRIAL REGISTRATION:Not applicable.

SUBMITTER: Ivic R

PROVIDER: S-EPMC7391698 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Serious conditions among patients with non-specific chief complaints in the pre-hospital setting: a retrospective cohort study.

Ivic Robert R Kurland Lisa L Vicente Veronica V Castrén Maaret M Bohm Katarina K

Scandinavian journal of trauma, resuscitation and emergency medicine 20200729 1

<h4>Background</h4>Emergency Medical Services (EMS) are faced daily with patients presenting with a non-specific chief complaints (NSC); i.e. decreased general condition, general malaise, sense of illness, or just being unable to cope with usual daily activities. Patients presenting with NSCs often have normal vital signs. It has previously been established that however, NSCs may have a serious underlying condition that has yet to be identified. The primary outcome of this study was to determine ...[more]

PMID: 32727586

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Project description:Objective:We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints. Materials and methods:Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k?=?1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE). Results:The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together. Discussion:Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space. Conclusion:Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

Project description:BACKGROUND:Although evidence from animal and human studies indicates opioid analgesics increase susceptibility to infections, it is unclear whether the risk varies by specific opioid. We compared the risk of serious infection among patients initiating long-acting opioid analgesics with and without previously reported immunosuppressive properties. METHODS:We conducted a retrospective cohort study of Tennessee Medicaid enrollees age ≥18 years initiating long-acting opioids (1995-2015). Hospitalizations for serious infection were identified using validated coding algorithms. We used multivariable Poisson regression models to calculate adjusted incidence rate ratios (aIRR) and 95% confidence intervals (CI) to compare the infection risk among patients using long-acting opioids with known immunosuppressive properties (morphine, fentanyl, methadone) to the infection risk among patients using long-acting opioids without known immunosuppressive properties (oxycodone, oxymorphone, tramadol) accounting for demographics, opioid dose, comorbidities and pain conditions, medication use, frailty indicators, and healthcare encounter history using exposure propensity scores. We further compared users of individual long-acting opioids to long-acting morphine users (considered the prototypical immunosuppressive opioid). RESULTS:Among the 61 240 patients initiating opioids with immunosuppressive properties and 22 811 patients initiating opioids without immunosuppressive properties, we identified 1906 serious infections. Nonimmunosuppressive opioid users had a lower rate of infections than immunosuppressive opioid users (aIRR:0.78 [CI: 0.66-0.91]). Among individual opioids, oxycodone users had a lower rate of infection than morphine users (aIRR:0.73 [CI: 0.60-0.89]). There were no significant differences in the infection risk between other opioids and morphine. CONCLUSION:The risk of serious infections among long-acting opioid users varies by opioid type. Providers should carefully consider the risk of serious infections when making pain management decisions.

Dataset Information

Serious conditions among patients with non-specific chief complaints in the pre-hospital setting: a retrospective cohort study.

Publications

Serious conditions among patients with non-specific chief complaints in the pre-hospital setting: a retrospective cohort study.

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