A Description of Mechanically Ventilated Patients Admitted From the Emergency Department With a Short Critical Care Stay.

BACKGROUND: Patients admitted to an intensive care unit (ICU) requiring invasive mechanical ventilation who are discharged alive from the ICU within 24 h are poorly characterized in the literature. OBJECTIVE: Our aim was to characterize a cohort of intubated emergency department (ED) patients who are extubated and discharged from the ICU within 24 h. METHODS: We conducted a retrospective, observational cohort study at a single level I trauma center from January 2017 to December 2019. We included adults who were admitted to an ICU from the ED requiring invasive mechanical ventilation. Our primary outcome was the proportion of patients who were discharged from the ICU alive within 24 h. RESULTS: Of 13,374 ED patients admitted to an ICU during the study period, 2871 patients were intubated and ventilated in the prehospital or ED settings. Of these, 14% were discharged alive from the ICU within 24 h of admission. Only 21% of these patients were intubated in the ED. We identified the following two distinct subpopulations comprising 62% of this short-stay group: patients with a primary discharge diagnosis of intoxication (47%) and minimally injured trauma patients (53%), with 4% of patients in both subgroups. CONCLUSIONS: A total of 14% of patients receiving intubation with mechanical ventilation in the prehospital environment or in the ED were discharged alive from the ICU within 24 h. We identified two distinct subgroups of patients with a short stay in intensive care who may be candidates for ED extubation, including patients with intoxication and minimally injured trauma patients.

Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning.

We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 patients with outpatient, emergency department, or inpatient anaphylaxis diagnosis codes during 2015-2019 in 2 integrated health-care institutions in the Northwest United States. We used one site's manually reviewed gold-standard outcomes data for model development and the other's for external validation based on cross-validated area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and sensitivity. In the development site 154 (64%) of 239 potential events met adjudication criteria for anaphylaxis compared with 180 (65%) of 277 in the validation site. Logistic regression models using only structured claims data achieved a cross-validated AUC of 0.58 (95% CI: 0.54, 0.63). Machine learning improved cross-validated AUC to 0.62 (0.58, 0.66); incorporating NLP-derived covariates further increased cross-validated AUCs to 0.70 (0.66, 0.75) in development and 0.67 (0.63, 0.71) in external validation data. A classification threshold with cross-validated PPV of 79% and cross-validated sensitivity of 66% in development data had cross-validated PPV of 78% and cross-validated sensitivity of 56% in external data. Machine learning and NLP-derived data improved identification of validated anaphylaxis events.

Validation of Acute Pancreatitis Among Adults in an Integrated Healthcare System.

BACKGROUND: Acute pancreatitis is a serious gastrointestinal disease that is an important target for drug safety surveillance. Little is known about the accuracy of ICD-10 codes for acute pancreatitis in the United States, or their performance in specific clinical settings. We conducted a validation study to assess the accuracy of acute pancreatitis ICD-10 diagnosis codes in inpatient, emergency department (ED), and outpatient settings. METHODS: We reviewed electronic medical records for encounters with acute pancreatitis diagnosis codes in an integrated healthcare system from October 2015 to December 2019. Trained abstractors and physician adjudicators determined whether events met criteria for acute pancreatitis. RESULTS: Out of 1,844 eligible events, we randomly sampled 300 for review. Across all clinical settings, 182 events met validation criteria for an overall positive predictive value (PPV) of 61% (95% confidence intervals [CI] = 55, 66). The PPV was 87% (95% CI = 79, 92%) for inpatient codes, but only 45% for ED (95% CI = 35, 54%) and outpatient (95% CI = 34, 55%) codes. ED and outpatient encounters accounted for 43% of validated events. Acute pancreatitis codes from any encounter type with lipase >3 times the upper limit of normal had a PPV of 92% (95% CI = 86, 95%) and identified 85% of validated events (95% CI = 79, 89%), while codes with lipase <3 times the upper limit of normal had a PPV of only 22% (95% CI = 16, 30%). CONCLUSIONS: These results suggest that ICD-10 codes accurately identified acute pancreatitis in the inpatient setting, but not in the ED and outpatient settings. Laboratory data substantially improved algorithm performance.

Identification and Validation of Anaphylaxis Using Electronic Health Data in a Population-based Setting.

BACKGROUND: Anaphylaxis is a life-threatening allergic reaction that is difficult to identify accurately with administrative data. We conducted a population-based validation study to assess the accuracy of ICD-10 diagnosis codes for anaphylaxis in outpatient, emergency department, and inpatient settings. METHODS: In an integrated healthcare system in Washington State, we obtained medical records from healthcare encounters with anaphylaxis diagnosis codes (potential events) from October 2015 to December 2018. To capture events missed by anaphylaxis diagnosis codes, we also obtained records on a sample of serious allergic and drug reactions. Two physicians determined whether potential events met established clinical criteria for anaphylaxis (validated events). RESULTS: Out of 239 potential events with anaphylaxis diagnosis codes, the overall positive predictive value (PPV) for validated events was 64% (95% CI = 58 to 70). The PPV decreased with increasing age. Common precipitants for anaphylaxis were food (39%), medications (35%), and insect bite or sting (12%). The sensitivity of emergency department and inpatient anaphylaxis diagnosis codes for all validated events was 58% (95% CI = 51 to 65), but sensitivity increased to 95% (95% CI = 74 to 99) when outpatient diagnosis codes were included. Using information from all validated events and sampling weights, the incidence rate for anaphylaxis was 3.6 events per 10,000 person-years (95% CI = 3.1 to 4.0). CONCLUSIONS: In this population-based setting, ICD-10 diagnosis codes for anaphylaxis from emergency department and inpatient settings had moderate PPV and sensitivity for validated events. These findings have implications for epidemiologic studies that seek to estimate risks of anaphylaxis using electronic health data.

Lysine 624 of the amyloid precursor protein (APP) is a critical determinant of amyloid ß peptide length: support for a sequential model of γ-secretase intramembrane proteolysis and regulation by the amyloid ß precursor protein (APP) juxtamembrane region.

γ-Secretase is a multiprotein intramembrane cleaving aspartyl protease (I-CLiP) that catalyzes the final cleavage of the amyloid ß precursor protein (APP) to release the amyloid ß peptide (Aß). Aß is the primary component of senile plaques in Alzheimer's disease (AD), and its mechanism of production has been studied intensely. γ-Secretase executes multiple cleavages within the transmembrane domain of APP, with cleavages producing Aß and the APP intracellular domain (AICD), referred to as γ and ε, respectively. The heterogeneous nature of the γ cleavage that produces various Aß peptides is highly relevant to AD, as increased production of Aß 1-42 is genetically and biochemically linked to the development of AD. We have identified an amino acid in the juxtamembrane region of APP, lysine 624, on the basis of APP695 numbering (position 28 relative to Aß) that plays a critical role in determining the final length of Aß peptides released by γ-secretase. Mutation of this lysine to alanine (K28A) shifts the primary site of γ-secretase cleavage from 1-40 to 1-33 without significant changes to ε cleavage. These results further support a model where ε cleavage occurs first, followed by sequential proteolysis of the remaining transmembrane fragment, but extend these observations by demonstrating that charged residues at the luminal boundary of the APP transmembrane domain limit processivity of γ-secretase.

Substrate-targeting gamma-secretase modulators.

Selective lowering of Abeta42 levels (the 42-residue isoform of the amyloid-beta peptide) with small-molecule gamma-secretase modulators (GSMs), such as some non-steroidal anti-inflammatory drugs, is a promising therapeutic approach for Alzheimer's disease. To identify the target of these agents we developed biotinylated photoactivatable GSMs. GSM photoprobes did not label the core proteins of the gamma-secretase complex, but instead labelled the beta-amyloid precursor protein (APP), APP carboxy-terminal fragments and amyloid-beta peptide in human neuroglioma H4 cells. Substrate labelling was competed by other GSMs, and labelling of an APP gamma-secretase substrate was more efficient than a Notch substrate. GSM interaction was localized to residues 28-36 of amyloid-beta, a region critical for aggregation. We also demonstrate that compounds known to interact with this region of amyloid-beta act as GSMs, and some GSMs alter the production of cell-derived amyloid-beta oligomers. Furthermore, mutation of the GSM binding site in the APP alters the sensitivity of the substrate to GSMs. These findings indicate that substrate targeting by GSMs mechanistically links two therapeutic actions: alteration in Abeta42 production and inhibition of amyloid-beta aggregation, which may synergistically reduce amyloid-beta deposition in Alzheimer's disease. These data also demonstrate the existence and feasibility of 'substrate targeting' by small-molecule effectors of proteolytic enzymes, which if generally applicable may significantly broaden the current notion of 'druggable' targets.