RESUMO
Rationale: Acute lung injury (ALI) carries a high risk of mortality but has no established pharmacologic therapy. We previously found that experimental ALI occurs through natural killer (NK) cell NKG2D receptor activation and that the cognate human ligand, MICB, was associated with ALI after transplantation. Objectives: To investigate the association of a common missense variant, MICBG406A, with ALI. Methods: We assessed MICBG406A genotypes within two multicenter observational study cohorts at risk for ALI: primary graft dysfunction (N = 619) and acute respiratory distress syndrome (N = 1,376). Variant protein functional effects were determined in cultured and ex vivo human samples. Measurements and Main Results: Recipients of MICBG406A-homozygous allografts had an 11.1% absolute risk reduction (95% confidence interval [CI], 3.2-19.4%) for severe primary graft dysfunction after lung transplantation and reduced risk for allograft failure (hazard ratio, 0.36; 95% CI, 0.13-0.98). In participants with sepsis, we observed 39% reduced odds of moderately or severely impaired oxygenation among MICBG406A-homozygous individuals (95% CI, 0.43-0.86). BAL NK cells were less frequent and less mature in participants with MICBG406A. Expression of missense variant protein MICBD136N in cultured cells resulted in reduced surface MICB and reduced NKG2D ligation relative to wild-type MICB. Coculture of variant MICBD136N cells with NK cells resulted in less NKG2D activation and less susceptibility to NK cell killing relative to the wild-type cells. Conclusions: These data support a role for MICB signaling through the NKG2D receptor in mediating ALI, suggesting a novel therapeutic approach.
Assuntos
Lesão Pulmonar Aguda , Disfunção Primária do Enxerto , Humanos , Lesão Pulmonar Aguda/genética , Genômica , Antígenos de Histocompatibilidade Classe I/genética , Antígenos de Histocompatibilidade Classe I/metabolismo , Subfamília K de Receptores Semelhantes a Lectina de Células NK/genética , Subfamília K de Receptores Semelhantes a Lectina de Células NK/metabolismoRESUMO
OBJECTIVES: Improving the efficiency of clinical trials in acute hypoxemic respiratory failure (HRF) depends on enrichment strategies that minimize enrollment of patients who quickly resolve with existing care and focus on patients at high risk for persistent HRF. We aimed to develop parsimonious models predicting risk of persistent HRF using routine data from ICU admission and select research immune biomarkers. DESIGN: Prospective cohorts for derivation ( n = 630) and external validation ( n = 511). SETTING: Medical and surgical ICUs at two U.S. medical centers. PATIENTS: Adults with acute HRF defined as new invasive mechanical ventilation (IMV) and hypoxemia on the first calendar day after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We evaluated discrimination, calibration, and practical utility of models predicting persistent HRF risk (defined as ongoing IMV and hypoxemia on the third calendar day after admission): 1) a clinical model with least absolute shrinkage and selection operator (LASSO) selecting Pa o2 /F io2 , vasopressors, mean arterial pressure, bicarbonate, and acute respiratory distress syndrome as predictors; 2) a model adding interleukin-6 (IL-6) to clinical predictors; and 3) a comparator model with Pa o2 /F io2 alone, representing an existing strategy for enrichment. Forty-nine percent and 69% of patients had persistent HRF in derivation and validation sets, respectively. In validation, both LASSO (area under the receiver operating characteristic curve, 0.68; 95% CI, 0.64-0.73) and LASSO + IL-6 (0.71; 95% CI, 0.66-0.76) models had better discrimination than Pa o2 /F io2 (0.64; 95% CI, 0.59-0.69). Both models underestimated risk in lower risk deciles, but exhibited better calibration at relevant risk thresholds. Evaluating practical utility, both LASSO and LASSO + IL-6 models exhibited greater net benefit in decision curve analysis, and greater sample size savings in enrichment analysis, compared with Pa o2 /F io2 . The added utility of LASSO + IL-6 model over LASSO was modest. CONCLUSIONS: Parsimonious, interpretable models that predict persistent HRF may improve enrichment of trials testing HRF-targeted therapies and warrant future validation.
Assuntos
Interleucina-6 , Insuficiência Respiratória , Adulto , Humanos , Estudos Prospectivos , Insuficiência Respiratória/terapia , Hipóxia/terapia , Unidades de Terapia IntensivaRESUMO
BACKGROUND: Hypoinflammatory and hyperinflammatory phenotypes have been identified in both Acute Respiratory Distress Syndrome (ARDS) and sepsis. Attributable mortality of ARDS in each phenotype of sepsis is yet to be determined. We aimed to estimate the population attributable fraction of death from ARDS (PAFARDS) in hypoinflammatory and hyperinflammatory sepsis, and to determine the primary cause of death within each phenotype. METHODS: We studied 1737 patients with sepsis from two prospective cohorts. Patients were previously assigned to the hyperinflammatory or hypoinflammatory phenotype using latent class analysis. The PAFARDS in patients with sepsis was estimated separately in the hypo and hyperinflammatory phenotypes. Organ dysfunction, severe comorbidities, and withdrawal of life support were abstracted from the medical record in a subset of patients from the EARLI cohort who died (n = 130/179). Primary cause of death was defined as the organ system that most directly contributed to death or withdrawal of life support. RESULTS: The PAFARDS was 19% (95%CI 10,28%) in hypoinflammatory sepsis and, 14% (95%CI 6,20%) in hyperinflammatory sepsis. Cause of death differed between the two phenotypes (p < 0.001). Respiratory failure was the most common cause of death in hypoinflammatory sepsis, whereas circulatory shock was the most common cause in hyperinflammatory sepsis. Death with severe underlying comorbidities was more frequent in hypoinflammatory sepsis (81% vs. 67%, p = 0.004). CONCLUSIONS: The PAFARDS is modest in both phenotypes whereas primary cause of death among patients with sepsis differed substantially by phenotype. This study identifies challenges in powering future clinical trials to detect changes in mortality outcomes among patients with sepsis and ARDS.
Assuntos
Fenótipo , Síndrome do Desconforto Respiratório , Sepse , Humanos , Sepse/mortalidade , Sepse/complicações , Sepse/fisiopatologia , Síndrome do Desconforto Respiratório/mortalidade , Síndrome do Desconforto Respiratório/fisiopatologia , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Estudos Prospectivos , Causas de Morte/tendências , Estudos de Coortes , InflamaçãoRESUMO
Understanding the contribution of genetic variation to drug response can improve the delivery of precision medicine. However, genome-wide association studies (GWAS) for drug response are uncommon and are often hindered by small sample sizes. We present a high-throughput framework to efficiently identify eligible patients for genetic studies of adverse drug reactions (ADRs) using "drug allergy" labels from electronic health records (EHRs). As a proof-of-concept, we conducted GWAS for ADRs to 14 common drug/drug groups with 81,739 individuals from Vanderbilt University Medical Center's BioVU DNA Biobank. We identified 7 genetic loci associated with ADRs at P < 5 × 10-8, including known genetic associations such as CYP2D6 and OPRM1 for CYP2D6-metabolized opioid ADR. Additional expression quantitative trait loci and phenome-wide association analyses added evidence to the observed associations. Our high-throughput framework is both scalable and portable, enabling impactful pharmacogenomic research to improve precision medicine.
Assuntos
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos/genética , Registros Eletrônicos de Saúde , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Estudo de Associação Genômica Ampla , Humanos , Farmacogenética , Medicina de PrecisãoRESUMO
INTRODUCTION: Infectious diseases are common causes of morbidity and mortality worldwide. Susceptibility to infection is highly heritable; however, little has been done to identify the genetic determinants underlying common infectious diseases. One GWAS was performed using 23andMe information about self-reported infections; we set out to confirm previous loci and identify new ones using medically diagnosed infections. METHODS: We used the electronic health record (EHR)-based biobank at Vanderbilt and diagnosis codes to identify cases of 12 infectious diseases in white patients: urinary tract infection, pneumonia, chronic sinus infections, otitis media, candidiasis, streptococcal pharyngitis, herpes zoster, herpes labialis, hepatitis B, infectious mononucleosis, tuberculosis (TB) or a positive TB test, and hepatitis C. We selected controls from patients with no diagnosis code for the candidate disease and matched by year of birth, sex, and calendar year at first and last EHR visits. We conducted GWAS using SAIGE and transcriptome-wide analysis (TWAS) using S-PrediXcan. We also conducted phenome-wide association study to understand associations between identified genetic variants and clinical phenotypes. RESULTS: We replicated three 23andMe loci (p ≤ 0.05): herpes zoster and rs7047299-A (p = 2.6 × 10-3) and rs2808290-C (p = 9.6 × 10-3;); otitis media and rs114947103-C (p = 0.04). We also identified 2 novel regions (p ≤ 5 × 10-8): rs113235453-G for otitis media (p = 3.04 × 10-8), and rs10422015-T for candidiasis (p = 3.11 × 10-8). In TWAS, four gene-disease associations were significant: SLC30A9 for otitis media (p = 8.06 × 10-7); LRP3 and WDR88 for candidiasis (p = 3.91 × 10-7 and p = 1.95 × 10-6); and AAMDC for hepatitis B (p = 1.51 × 10-6). CONCLUSION: We conducted GWAS and TWAS for 12 infectious diseases and identified novel genetic contributors to the susceptibility of infectious diseases.
Assuntos
Candidíase , Doenças Transmissíveis , Hepatite B , Herpes Zoster , Otite Média , Bancos de Espécimes Biológicos , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Otite Média/genética , Polimorfismo de Nucleotídeo ÚnicoRESUMO
Noninvasive sampling of the distal airspace in patients with acute respiratory distress syndrome (ARDS) has long eluded clinical and translational researchers. We recently reported that fluid collected from heat moisture exchange (HME) filters closely mirrors fluid directly aspirated from the distal airspace. In the current study, we sought to determine fluid yield from different HME types, optimal HME circuit dwell time, and reliability of HME fluid in reflecting the distal airspace. We studied fluid yield from four different filter types by loading increasing volumes of saline and measuring volumes of fluid recovered. We collected filters after 1, 2, and 4 h of dwell time for measurement of fluid volume and total protein from 13 subjects. After identifying 4 h as the optimal dwell time, we measured total protein and IgM in HME fluid from 42 subjects with ARDS and nine with hydrostatic pulmonary edema (HYDRO). We found that the fluid yield varies greatly by filter type. With timed sample collection, fluid recovery increased with increasing circuit dwell time with a median volume of 2.0 mL [interquartile range (IQR) 1.2-2.7] after 4 h. Total protein was higher in the 42 subjects with ARDS compared with nine with HYDRO [median 708 µg/mL (IQR 244-2017) vs. 364 µg/mL (IQR 136-578), P = 0.047], confirming that total protein concentration in HME is higher in ARDS compared with hydrostatic edema. These studies establish a standardized HME fluid collection protocol and confirm that HME fluid analysis is a novel noninvasive tool for the study of the distal airspace in ARDS.
Assuntos
Técnicas de Diagnóstico do Sistema Respiratório/normas , Temperatura Alta , Umidade , Edema Pulmonar/diagnóstico , Respiração Artificial/métodos , Síndrome do Desconforto Respiratório/diagnóstico , Adulto , Idoso , Idoso de 80 Anos ou mais , Testes Respiratórios , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Edema Pulmonar/fisiopatologia , Síndrome do Desconforto Respiratório/fisiopatologiaRESUMO
Rationale: Acute respiratory distress syndrome (ARDS) lacks known causal biomarkers. Plasma concentrations of sRAGE (soluble receptor for advanced glycation end products) strongly associate with ARDS risk. However, whether plasma sRAGE contributes causally to ARDS remains unknown.Objectives: Evaluate plasma sRAGE as a causal intermediate in ARDS by Mendelian randomization (MR), a statistical method to infer causality using observational data.Methods: We measured early plasma sRAGE in two critically ill populations with sepsis. The cohorts were whole-genome genotyped and phenotyped for ARDS. To select validated genetic instruments for MR, we regressed plasma sRAGE on genome-wide genotypes in both cohorts. The causal effect of plasma sRAGE on ARDS was inferred using the top variants with significant associations in both populations (P < 0.01, R2 > 0.02). We applied the inverse variance-weighted method to obtain consistent estimates of the causal effect of plasma sRAGE on ARDS risk.Measurements and Main Results: There were 393 European and 266 African ancestry patients in the first cohort and 843 European ancestry patients in the second cohort. Plasma sRAGE was strongly associated with ARDS risk in both populations (odds ratio, 1.86; 95% confidence interval [1.54-2.25]; 2.56 [2.14-3.06] per log increase). Using genetic instruments common to both populations, plasma sRAGE had a consistent causal effect on ARDS risk with a ß estimate of 0.50 (95% confidence interval [0.09-0.91] per log increase).Conclusions: Plasma sRAGE is genetically regulated during sepsis, and MR analysis indicates that increased plasma sRAGE leads to increased ARDS risk, suggesting plasma sRAGE acts as a causal intermediate in sepsis-related ARDS.
Assuntos
Biomarcadores/sangue , Receptor para Produtos Finais de Glicação Avançada/genética , Síndrome do Desconforto Respiratório/sangue , Síndrome do Desconforto Respiratório/fisiopatologia , Sepse/sangue , Sepse/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , População Negra/genética , Estudos de Coortes , Estado Terminal , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fatores de Risco , Sepse/fisiopatologia , População Branca/genéticaRESUMO
OBJECTIVES: New-onset atrial fibrillation during critical illness is an independent risk factor for mortality. The ability to identify patients at high risk for new-onset atrial fibrillation is limited. We hypothesized that genetic susceptibility contributes to risk of new-onset atrial fibrillation in the ICU. DESIGN: Retrospective sub-study of a prospective observational cohort study. SETTING: Medical and general surgical ICUs in a tertiary academic medical center. PATIENTS: One-thousand three-hundred sixty-nine critically ill patients admitted to the ICU for at least 2 days with no known history of atrial fibrillation who had DNA available for genotyping. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We genotyped 21 single-nucleotide polymorphisms associated with atrial fibrillation in ambulatory studies using a Sequenom platform (San Diego, CA). We collected demographics, medical history, and development of new-onset atrial fibrillation during the first four days of ICU admission. New-onset atrial fibrillation occurred in 98 patients (7.2%) and was associated with age, male sex, coronary artery disease, and vasopressor use. Single-nucleotide polymorphisms associated with new-onset atrial fibrillation were rs3853445 (near PITX2, p = 0.0002), rs6838973 (near PITX2, p = 0.01), and rs12415501 (in NEURL, p = 0.03) on univariate testing. When controlling for clinical factors, rs3853445 (odds ratio, 0.47; 95% CI, 0.30-0.73; p = 0.001) and rs12415501 (odds ratio, 1.72; 95% CI, 1.27-2.59; p = 0.01) remained significantly associated with new-onset atrial fibrillation. The addition of genetic variables to clinical factors improved new-onset atrial fibrillation discrimination in a multivariable logistic regression model (C-statistic 0.82 vs 0.78; p = 0.0009). CONCLUSIONS: We identified several single-nucleotide polymorphisms associated with new-onset atrial fibrillation in a large cohort of critically ill ICU patients, suggesting there is genetic susceptibility underlying this common clinical condition. This finding may provide new targets for future mechanistic studies and additional insight into the application of genomic information to identify patients at elevated risk for a common and important condition in the ICU.
Assuntos
Fibrilação Atrial/genética , Predisposição Genética para Doença , Idoso , Estudos de Coortes , Estado Terminal , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Polimorfismo de Nucleotídeo Único , Estudos Prospectivos , Estudos Retrospectivos , Fatores de RiscoAssuntos
Anemia , COVID-19 , Linfopenia , Trombocitopenia , Humanos , COVID-19/complicações , SARS-CoV-2 , Linfopenia/complicações , Anemia/complicaçõesRESUMO
RATIONALE: A major barrier to a more complete understanding of acute respiratory distress syndrome (ARDS) pathophysiology is the inability to sample the distal airspace of patients with ARDS. The heat moisture exchanger (HME) filter is an inline bacteriostatic sponge that collects exhaled moisture from the lungs of mechanically ventilated patients. OBJECTIVES: To test the hypothesis that HME filter fluid (HMEF) represents the distal airspace fluid in patients with ARDS. METHODS: Samples of HMEF were collected from 37 patients with acute pulmonary edema (either from ARDS or hydrostatic causes [HYDRO; control subjects]). Concurrent undiluted pulmonary edema fluid (EF) and HMEF were collected from six patients. HMEF from 11 patients (8 ARDS and 3 HYDRO) were analyzed by liquid chromatography-coupled tandem mass spectometry. Total protein (bicinchoninic acid assay), MMP-9 (matrix metalloproteinase-9), and MPO (myeloperoxidase) (ELISA) were measured in 29 subjects with ARDS and 5 subjects with HYDRO. SP-D (surfactant protein-D), RAGE (receptor for advanced glycation end-products) (ELISA), and cytokines (IL-1ß, IL-6, IL-8, and tumor necrosis factor-α) (electrochemiluminescent assays) were measured in six concurrent HMEF and EF samples. MEASUREMENTS AND MAIN RESULTS: Liquid chromatography-coupled tandem mass spectrometry on concurrent EF and HMEF samples from four patients revealed similar base peak intensities and m/z values indicating similar protein composition. There were 21 significantly elevated proteins in HMEF from patients with ARDS versus HYDRO. Eight proteins measured in concurrent EF and HMEF from six patients were highly correlated. In HMEF, total protein and MMP-9 were significantly higher in ARDS than in HYDRO. CONCLUSIONS: These data suggest that HMEF is a novel, noninvasive method to accurately sample the distal airspace in patients with ARDS.
Assuntos
Técnicas de Diagnóstico do Sistema Respiratório , Esponja de Gelatina Absorvível , Procedimentos Cirúrgicos Minimamente Invasivos/instrumentação , Procedimentos Cirúrgicos Minimamente Invasivos/métodos , Alvéolos Pulmonares/fisiopatologia , Respiração Artificial/métodos , Síndrome do Desconforto Respiratório/diagnóstico , Síndrome do Desconforto Respiratório/fisiopatologia , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-IdadeRESUMO
Objectives: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. Materials and Methods: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. Results: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). Conclusion: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
RESUMO
OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts. MATERIALS AND METHODS: We prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (ie, type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network. RESULTS: GPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values). CONCLUSION: GPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
RESUMO
Sepsis accounts for one in three hospital deaths. Higher concentrations of high-density lipoprotein cholesterol (HDL-C) are associated with apparent protection from sepsis, suggesting a potential therapeutic role for HDL-C or drugs, such as cholesteryl ester transport protein (CETP) inhibitors that increase HDL-C. However, these beneficial clinical associations might be due to confounding; genetic approaches can address this possibility. We identified 73,406 White adults admitted to Vanderbilt University Medical Center with infection; 11,612 had HDL-C levels, and 12,377 had genotype information from which we constructed polygenic risk scores (PRS) for HDL-C and the effect of CETP on HDL-C. We tested the associations between predictors (measured HDL-C, HDL-C PRS, CETP PRS, and rs1800777) and outcomes: sepsis, septic shock, respiratory failure, and in-hospital death. In unadjusted analyses, lower measured HDL-C concentrations were significantly associated with increased risk of sepsis (p = 2.4 × 10-23 ), septic shock (p = 4.1 × 10-12 ), respiratory failure (p = 2.8 × 10-8 ), and in-hospital death (p = 1.0 × 10-8 ). After adjustment (age, sex, electronic health record length, comorbidity score, LDL-C, triglycerides, and body mass index), these associations were markedly attenuated: sepsis (p = 2.6 × 10-3 ), septic shock (p = 8.1 × 10-3 ), respiratory failure (p = 0.11), and in-hospital death (p = 4.5 × 10-3 ). HDL-C PRS, CETP PRS, and rs1800777 significantly predicted HDL-C (p < 2 × 10-16 ), but none were associated with sepsis outcomes. Concordant findings were observed in 13,254 Black patients hospitalized with infections. Lower measured HDL-C levels were significantly associated with increased risk of sepsis and related outcomes in patients with infection, but a causal relationship is unlikely because no association was found between the HDL-C PRS or the CETP PRS and the risk of adverse sepsis outcomes.
Assuntos
Sepse , Choque Séptico , Adulto , Humanos , HDL-Colesterol/genética , HDL-Colesterol/metabolismo , Proteínas de Transferência de Ésteres de Colesterol/genética , Proteínas de Transferência de Ésteres de Colesterol/metabolismo , Mortalidade Hospitalar , LDL-Colesterol/metabolismo , Sepse/genéticaRESUMO
Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.
Assuntos
Boxe , COVID-19 , Saúde da População , Humanos , Registros Eletrônicos de Saúde , Síndrome de COVID-19 Pós-Aguda , Reprodutibilidade dos Testes , Aprendizado de Máquina , FenótipoRESUMO
BACKGROUND: In sepsis and acute respiratory distress syndrome (ARDS), heterogeneity has contributed to difficulty identifying effective pharmacotherapies. In ARDS, two molecular phenotypes (hypoinflammatory and hyperinflammatory) have consistently been identified, with divergent outcomes and treatment responses. In this study, we sought to derive molecular phenotypes in critically ill adults with sepsis, determine their overlap with previous ARDS phenotypes, and evaluate whether they respond differently to treatment in completed sepsis trials. METHODS: We used clinical data and plasma biomarkers from two prospective sepsis cohorts, the Validating Acute Lung Injury biomarkers for Diagnosis (VALID) study (N=1140) and the Early Assessment of Renal and Lung Injury (EARLI) study (N=818), in latent class analysis (LCA) to identify the optimal number of classes in each cohort independently. We used validated models trained to classify ARDS phenotypes to evaluate concordance of sepsis and ARDS phenotypes. We applied these models retrospectively to the previously published Prospective Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis and Septic Shock (PROWESS-SHOCK) trial and Vasopressin and Septic Shock Trial (VASST) to assign phenotypes and evaluate heterogeneity of treatment effect. FINDINGS: A two-class model best fit both VALID and EARLI (p<0·0001). In VALID, 804 (70·5%) of the 1140 patients were classified as hypoinflammatory and 336 (29·5%) as hyperinflammatory; in EARLI, 530 (64·8%) of 818 were hypoinflammatory and 288 (35·2%) hyperinflammatory. We observed higher plasma pro-inflammatory cytokines, more vasopressor use, more bacteraemia, lower protein C, and higher mortality in the hyperinflammatory than in the hypoinflammatory phenotype (p<0·0001 for all). Classifier models indicated strong concordance between sepsis phenotypes and previously identified ARDS phenotypes (area under the curve 0·87-0·96, depending on the model). Findings were similar excluding participants with both sepsis and ARDS. In PROWESS-SHOCK, 1142 (68·0%) of 1680 patients had the hypoinflammatory phenotype and 538 (32·0%) had the hyperinflammatory phenotype, and response to activated protein C differed by phenotype (p=0·0043). In VASST, phenotype proportions were similar to other cohorts; however, no treatment interaction with the type of vasopressor was observed (p=0·72). INTERPRETATION: Molecular phenotypes previously identified in ARDS are also identifiable in multiple sepsis cohorts and respond differently to activated protein C. Molecular phenotypes could represent a treatable trait in critical illness beyond the patient's syndromic diagnosis. FUNDING: US National Institutes of Health.
Assuntos
Síndrome do Desconforto Respiratório , Sepse , Choque Séptico , Adulto , Humanos , Choque Séptico/diagnóstico , Choque Séptico/tratamento farmacológico , Proteína C/uso terapêutico , Estudos Retrospectivos , Estudos Prospectivos , Sepse/diagnóstico , Sepse/tratamento farmacológico , Sepse/complicações , Fenótipo , Biomarcadores , Vasoconstritores/uso terapêutico , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
The field of immunogenomics has the opportunity for accelerated genetic discovery aided by the maturation of electronic health records (EHRs) linked to DNA biobanks. Novel analysis methods in deep phenotyping of EHR data allow the full realization of the paired and increasingly dense genetic/phenotypic information available. This enables researchers to uncover genetic risk factors for the prevention and optimal treatment of immune-mediated diseases and immune-mediated adverse drug reactions. This article reviews the background of EHRs linked to DNA biobanks, potential applications to immunogenomic discovery, and current and emerging techniques in EHR-based deep phenotyping.
Assuntos
Registros Eletrônicos de Saúde , Variação Genética , Genótipo , Humanos , FenótipoRESUMO
Acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure yet has few pharmacologic therapies, reflecting the mechanistic heterogeneity of lung injury. We hypothesized that damage to the alveolar epithelial glycocalyx, a layer of glycosaminoglycans interposed between the epithelium and surfactant, contributes to lung injury in patients with ARDS. Using mass spectrometry of airspace fluid noninvasively collected from mechanically ventilated patients, we found that airspace glycosaminoglycan shedding (an index of glycocalyx degradation) occurred predominantly in patients with direct lung injury and was associated with duration of mechanical ventilation. Male patients had increased shedding, which correlated with airspace concentrations of matrix metalloproteinases. Selective epithelial glycocalyx degradation in mice was sufficient to induce surfactant dysfunction, a key characteristic of ARDS, leading to microatelectasis and decreased lung compliance. Rapid colorimetric quantification of airspace glycosaminoglycans was feasible and could provide point-of-care prognostic information to clinicians and/or be used for predictive enrichment in clinical trials.
Assuntos
Glicocálix/metabolismo , Glicosaminoglicanos , Atelectasia Pulmonar , Síndrome do Desconforto Respiratório , Células Epiteliais Alveolares/metabolismo , Células Epiteliais Alveolares/patologia , Animais , Duração da Terapia , Feminino , Glicosaminoglicanos/análise , Glicosaminoglicanos/metabolismo , Humanos , Doenças Pulmonares Intersticiais/etiologia , Doenças Pulmonares Intersticiais/metabolismo , Masculino , Camundongos , Valor Preditivo dos Testes , Prognóstico , Atelectasia Pulmonar/diagnóstico , Atelectasia Pulmonar/etiologia , Atelectasia Pulmonar/prevenção & controle , Reprodutibilidade dos Testes , Respiração Artificial/efeitos adversos , Respiração Artificial/métodos , Síndrome do Desconforto Respiratório/diagnóstico , Síndrome do Desconforto Respiratório/etiologia , Síndrome do Desconforto Respiratório/metabolismo , Fatores SexuaisRESUMO
The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.
Assuntos
Pesquisa Biomédica/métodos , Bases de Conhecimento , Processamento de Linguagem Natural , Prescrições de Medicamentos/estatística & dados numéricos , Registros Eletrônicos de Saúde/estatística & dados numéricos , HumanosRESUMO
Acute respiratory distress syndrome is underrecognized in the ICU, but it remains uncertain if acute respiratory distress syndrome recognition affects evidence-based acute respiratory distress syndrome care in the modern era. We sought to determine the rate of clinician-recognized acute respiratory distress syndrome in an academic medical ICU and understand how clinician-recognized-acute respiratory distress syndrome affects clinical care and patient-centered outcomes. DESIGN: Observational cohort study. SETTING: Single medical ICU at an academic tertiary-care hospital. PATIENTS: Nine hundred seventy-seven critically ill adults (381 with expert-adjudicated acute respiratory distress syndrome) enrolled from 2006 to 2015. INTERVENTIONS: Clinician-recognized-acute respiratory distress syndrome was identified using an electronic keyword search of clinical notes in the electronic health record. We assessed the classification performance of clinician-recognized acute respiratory distress syndrome for identifying expert-adjudicated acute respiratory distress syndrome. We also compared differences in ventilator settings, diuretic prescriptions, and cumulative fluid balance between clinician-recognized acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome. MEASUREMENTS AND MAIN RESULTS: Overall, clinician-recognized-acute respiratory distress syndrome had a sensitivity of 47.5%, specificity 91.1%, positive predictive value 77.4%, and negative predictive value 73.1% for expert-adjudicated acute respiratory distress syndrome. Among the 381 expert-adjudicated acute respiratory distress syndrome cases, we did not observe any differences in ventilator tidal volumes between clinician-recognized-acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome, but clinician-recognized-acute respiratory distress syndrome patients had a more negative cumulative fluid balance (mean difference, -781 mL; 95% CI, [-1,846 to +283]) and were more likely to receive diuretics (49.3% vs 35.7%, p = 0.02). There were no differences in mortality, ICU length of stay, or ventilator-free days. CONCLUSIONS: Acute respiratory distress syndrome recognition was low in this single-center study. Although acute respiratory distress syndrome recognition was not associated with lower ventilator volumes, it was associated with differences in behaviors related to fluid management. These findings have implications for the design of future studies promoting evidence-based acute respiratory distress syndrome interventions in the ICU.