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OBJECTIVES: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes. METHODS: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset. RESULTS: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2. CONCLUSION: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
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Autoanticorpos , Lúpus Eritematoso Sistêmico , Humanos , Anticorpos Antinucleares , DNA , Imunossupressores , Aprendizado de MáquinaRESUMO
BACKGROUND: Deep learning models have had a lot of success in various fields. However, on structured data they have struggled. Here we apply four state-of-the-art supervised deep learning models using the attention mechanism and compare against logistic regression and XGBoost using discrimination, calibration and clinical utility. METHODS: We develop the models using a general practitioners database. We implement a recurrent neural network, a transformer with and without reverse distillation and a graph neural network. We measure discrimination using the area under the receiver operating characteristic curve (AUC) and the area under the precision recall curve (AUPRC). We assess smooth calibration using restricted cubic splines and clinical utility with decision curve analysis. RESULTS: Our results show that deep learning approaches can improve discrimination up to 2.5% points AUC and 7.4% points AUPRC. However, on average the baselines are competitive. Most models are similarly calibrated as the baselines except for the graph neural network. The transformer using reverse distillation shows the best performance in clinical utility on two out of three prediction problems over most of the prediction thresholds. CONCLUSION: In this study, we evaluated various approaches in supervised learning using neural networks and attention. Here we do a rigorous comparison, not only looking at discrimination but also calibration and clinical utility. There is value in using deep learning models on electronic health record data since it can improve discrimination and clinical utility while providing good calibration. However, good baseline methods are still competitive.
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Registros Eletrônicos de Saúde , Redes Neurais de Computação , Humanos , Modelos Logísticos , Curva ROC , Área Sob a CurvaRESUMO
BACKGROUND AND AIM: Cholangiocarcinoma (CCA) is an often fatal primary cancer of the liver that tends to be resistant to chemotherapy. Multidrug resistance proteins (MRPs) contribute to the chemoresistance of these tumors. The objectives of the study were to document MRP expression profiles in two representative human intrahepatic and extrahepatic CCA cells lines (HuCCT1 and KMBC, respectively) and gemcitabine-induced cytotoxicity prior to and following MRP knockdown. METHODS: Multidrug resistance protein mRNA and protein expression were documented by real-time reverse transcription-polymerase chain reaction and western blots, respectively. MRP knockdown was achieved with lentivirus small hairpin RNA constructs. RESULTS: Prior to gemcitabine exposure, MRP1, MRP2, MRP4, MRP5, and MRP6 mRNA were expressed in HuCCT1 cells and MRP1, MRP3, MRP4, and MRP5 in KMBC cells. Following gemcitabine exposure, MRP5 and MRP6 expressions were significantly upregulated in HuCCT1 cells and MRP5 in KMBC cells. In HuCCT1 cells, although MRP5 knockdown had no effect, MRP6 knockdown significantly increased gemcitabine-induced cytotoxicity. In KMBC cells, MRP5 knockdown significantly increased gemcitabine cytotoxicity. CONCLUSIONS: Inhibition of MRP6 expression in intra-hepatic and MRP5 in extra-hepatic should be explored as potential treatments for CCA in humans.
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Subfamília B de Transportador de Cassetes de Ligação de ATP/genética , Antimetabólitos Antineoplásicos/toxicidade , Colangiocarcinoma/genética , Colangiocarcinoma/patologia , Desoxicitidina/análogos & derivados , Resistencia a Medicamentos Antineoplásicos/genética , Expressão Gênica/efeitos dos fármacos , Técnicas de Silenciamento de Genes , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patologia , Subfamília B de Transportador de Cassetes de Ligação de ATP/metabolismo , Linhagem Celular Tumoral , Desoxicitidina/toxicidade , Técnicas de Silenciamento de Genes/métodos , Humanos , Fígado/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Reação em Cadeia da Polimerase Via Transcriptase Reversa , GencitabinaRESUMO
The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.
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COVID-19 , Mídias Sociais , Humanos , Armazenamento e Recuperação da Informação , Pandemias , SARS-CoV-2RESUMO
INTRODUCTION AND OBJECTIVES: Intrahepatic (I-CCA) and extrahepatic (E-CCA) cholangiocarcinoma (CCA) have different growth patterns and risks for tumor metastasis. Inhibition and/or activation of the chemokine receptor CCR subclasses have been reported to alter tumor cell biology in non-CCA cancers. In this study we documented CCR expression profiles in representative human I-CCA and E-CCA cell lines and the in vitro effects of CCR antagonists and agonists on tumor cell biology. MATERIALS AND METHODS: CCR expression profiles were documented by real-time reverse transcription polymerase chain reaction; cell proliferation by WST-1; spheroid formation by sphere dimensions in anchorage-free medium; cell migration by wound healing and invasion by Transwell invasion chambers. RESULTS: All 10 CCR motifs (CCR1-10) were expressed in the I-CCA, HuCCT1 cell line and six (CCR4, 5, 6, 8, 9 and 10) in the E-CCA, KMBC cell line. In HuCCT1 cells, CCR5 expression was most abundant whereas in KMBC cells, CCR6 followed by CCR5 were most abundant. The CCR5 antagonist Maraviroc significantly inhibited cell proliferation, migration and invasion in HuCCT1 cells, and spheroid formation and invasion in KMBC cells. The CCR5 agonist RANTES had no effect on HuCCT1 cells but increased cell proliferation, migration and invasion of KMBC cells. CONCLUSION: These results suggest that CCR expression profiles differ in I-CCA and E-CCA. They also indicate that CCR5 antagonists and agonists have cell-specific effects but in general, CCR5 inactivation inhibits CCA tumor cell aggressiveness. Additional research is required to determine whether CCR5 inactivation is of value in the treatment of CCA in humans.
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Neoplasias dos Ductos Biliares/genética , Ductos Biliares Extra-Hepáticos/patologia , Ductos Biliares Intra-Hepáticos/patologia , Colangiocarcinoma/genética , DNA de Neoplasias/genética , Regulação Neoplásica da Expressão Gênica , Receptores CCR5/genética , Neoplasias dos Ductos Biliares/metabolismo , Neoplasias dos Ductos Biliares/patologia , Ductos Biliares Extra-Hepáticos/metabolismo , Ductos Biliares Intra-Hepáticos/metabolismo , Biomarcadores Tumorais/biossíntese , Biomarcadores Tumorais/genética , Linhagem Celular Tumoral , Proliferação de Células/genética , Colangiocarcinoma/metabolismo , Colangiocarcinoma/patologia , DNA de Neoplasias/metabolismo , Humanos , Receptores CCR5/biossíntese , Transdução de SinaisRESUMO
INTRODUCTION AND OBJECTIVES: Hepatocellular carcinoma (HCC) can recur following radiofrequency ablation and other hyperthermic treatment modalities. Cancer stem cells (CSCs) are a subpopulation of HCC cells that are difficult to eradicate and largely responsible for tumor recurrences. Thus, the principal objective of this study was to determine whether human HCC CSCs are relatively thermal-resistant compared to non-stem or mature cancer cells (MCCs). MATERIALS AND METHODS: Epithelial cell adhesion molecule (EpCAM) positive enriched CSCs and EpCAM- MCCs were derived from a human HCC cell line using fluorescence activated cell sorting. Each cell population was exposed to 65°C heat for 0-16min and survival documented at various time points. RESULTS: Cell survival curves were similar in CSC and MCCs throughout the 16min heat exposure period. Maximum killing was obtained after 12-14min of heat exposure. Cytoprotective, heat shock proteins-70 (HSP70) and -90 (HSP90) mRNA expression were not disproportionately increased in CSCs. CONCLUSIONS: These results suggest that human HCC CSCs are not more thermal resistant than MCCs and therefore, do not support the hypothesis that HCC recurrences following hyperthermic treatment reflect CSC thermal-resistance.
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Carcinoma Hepatocelular/cirurgia , Temperatura Alta , Neoplasias Hepáticas/cirurgia , Células-Tronco Neoplásicas/metabolismo , Carcinoma Hepatocelular/genética , Sobrevivência Celular , Proteínas de Choque Térmico HSP70/genética , Proteínas de Choque Térmico HSP90/genética , Células Hep G2 , Humanos , Terapia a Laser , Neoplasias Hepáticas/genética , Recidiva Local de Neoplasia , RNA Mensageiro/metabolismo , Ablação por RadiofrequênciaRESUMO
BACKGROUND: Interventions to reduce readmissions after acute heart failure hospitalization require early identification of patients. The purpose of this study was to develop and test accuracies of various approaches to identify patients with acute decompensated heart failure (ADHF) with the use of data derived from the electronic health record. METHODS AND RESULTS: We included 37,229 hospitalizations of adult patients at a single hospital during 2013-2015. We developed 4 algorithms to identify hospitalization with a principal discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic regression of 31 structured data elements, 3) machine learning with unstructured data, and 4) machine learning with the use of both structured and unstructured data. In data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value (PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based on a brief survey of 3 providers who perform chart review for ADHF, we estimated that providers spent 8.6 minutes per chart review; using this this parameter, we estimated that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and 4, respectively. CONCLUSIONS: Machine learning algorithms with unstructured notes had the best performance for identification of ADHF and can improve provider efficiency for delivery of quality improvement interventions.
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Algoritmos , Diagnóstico Precoce , Registros Eletrônicos de Saúde/estatística & dados numéricos , Insuficiência Cardíaca/diagnóstico , Hospitalização/estatística & dados numéricos , Aprendizado de Máquina , Sistema de Registros , Doença Aguda , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Melhoria de Qualidade , Estudos RetrospectivosRESUMO
OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND METHODS: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment. RESULTS: The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. DISCUSSION: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings. CONCLUSION: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.
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Registros Eletrônicos de Saúde , Aprendizado de Máquina , Oncologia , Humanos , Modelos Logísticos , Metadados , Auditoria Médica , Estudo de Prova de ConceitoRESUMO
Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.g., ixazomib, lenalidomide, dexamethasone (IRd) vs lenalidomide, dexamethasone (Rd). Using TOURMALINE trial data, we trained and internally validated our model on newly diagnosed myeloma patients (N = 703) and externally validated it on relapsed and refractory myeloma patients (N = 720). Our model achieved superior performance to a risk model based on the multiple myeloma international staging system (ISS) (p < 0.001, Bonferroni corrected) and comparable performance to survival models trained separately on each task, but unable to forecast biomarkers. Our approach outperformed state-of-the-art deep learning models, tailored towards forecasting, on predicting key disease biomarkers (p < 0.001, Bonferroni corrected). Finally, leveraging our model's capacity to estimate individual-level treatment effects, we found that patients with IgA kappa myeloma appear to benefit the most from IRd. Our study suggests that a holistic assessment of a patient's myeloma course is possible, potentially serving as the foundation for a personalized decision support system.
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BACKGROUND: Preclinical sepsis models have been criticized for their inability to recapitulate human sepsis and suffer from methodological shortcomings that limit external validity and reproducibility. The National Preclinical Sepsis Platform (NPSP) is a consortium of basic science researchers, veterinarians, and stakeholders in Canada undertaking standardized multi-laboratory sepsis research to increase the efficacy and efficiency of bench-to-bedside translation. In this study, we aimed to develop and characterize a 72-h fecal-induced peritonitis (FIP) model of murine sepsis conducted in two independent laboratories. The experimental protocol was optimized by sequentially modifying dose of fecal slurry and timing of antibiotics in an iterative fashion, and then repeating the experimental series at site 1 and site 2. RESULTS: Escalating doses of fecal slurry (0.5-2.5 mg/g) resulted in increased disease severity, as assessed by the modified Murine Sepsis Score (MSS). However, the MSS was poorly associated with progression to death during the experiments, and mice were found dead without elevated MSS scores. Administration of early antibiotics within 4 h of inoculation rescued the animals from sepsis compared with late administration of antibiotics after 12 h, as evidenced by 100% survival and reduced bacterial load in peritoneum and blood in the early antibiotic group. Site 1 and site 2 had statistically significant differences in mortality (60% vs 88%; p < 0.05) for the same dose of fecal slurry (0.75 mg/g) and marked differences in body temperature between groups. CONCLUSIONS: We demonstrate a systematic approach to optimizing a 72-h FIP model of murine sepsis for use in multi-laboratory studies. Alterations to experimental conditions, such as dose of fecal slurry and timing of antibiotics, have clear impact on outcomes. Differences in mortality between sites despite rigorous standardization warrants further investigations to better understand inter-laboratory variation and methodological design in preclinical studies.
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Aim of the study: Intra- and extrahepatic cholangiocarcinoma (I-CCA and E-CCA respectively) exhibit different growth features that contribute to different clinical outcomes. Cancer stem cells (CSCs) influence tumor growth and thereby may be responsible for these differences. The aim of this study was to document and compare the growth features of human I-CCA and E-CCA cell lines and determine whether any differences observed could be explained by differences in the prevalence and/or stem cell surface marker (SCSM) expression profiles of CSCs within the tumor cell lines. Material and methods: Six CCA cells lines, three I-CCA and three E-CCA, were studied. Tumor cell growth features including cell proliferation, colony/spheroid formation, migration and invasion were documented. CSC prevalence and SCSM expression profiles were examined by flow cytometry. Results: I-CCA cells had significantly increased proliferative activity, shorter doubling times and were more invasive than E-CCA cells, while colony/spheroid formation and migration were similar in the two cell populations. There were no significant differences in CSC prevalence rates or SCSM expression profiles. Conclusions: These findings suggest that I-CCA cells proliferate at a more rapid rate and are more invasive than E-CCA cells but the differences cannot be explained by differences in the prevalence or SCSM expression profiles of CSCs within the tumor cell population.
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As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and used in clinical and business decisions. We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest mechanisms for identifying and dealing with bias and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.
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Ecossistema , Seguradoras , Algoritmos , Viés , Humanos , Aprendizado de MáquinaRESUMO
Importance: Crisis standards of care (CSOC) scores designed to allocate scarce resources during the COVID-19 pandemic could exacerbate racial disparities in health care. Objective: To analyze the association of a CSOC scoring system with resource prioritization and estimated excess mortality by race, ethnicity, and residence in a socially vulnerable area. Design, Setting, and Participants: This retrospective cohort analysis included adult patients in the intensive care unit during a regional COVID-19 surge from April 13 to May 22, 2020, at 6 hospitals in a health care network in greater Boston, Massachusetts. Participants were scored by acute severity of illness using the Sequential Organ Failure Assessment score and chronic severity of illness using comorbidity and life expectancy scores, and only participants with complete scores were included. The score was ordinal, with cutoff points suggested by the Massachusetts guidelines. Exposures: Race, ethnicity, Social Vulnerability Index. Main Outcomes and Measures: The primary outcome was proportion of patients in the lowest priority score category stratified by self-reported race. Secondary outcomes were discrimination and calibration of the score overall and by race, ethnicity, and neighborhood Social Vulnerability Index. Projected excess deaths were modeled by race, using the priority scoring system and a random lottery. Results: Of 608 patients in the intensive care unit during the study period, 498 had complete data and were included in the analysis; this population had a median (IQR) age of 67 (56-75) years, 191 (38.4%) female participants, 79 (15.9%) Black participants, and 225 patients (45.7%) with COVID-19. The area under the receiver operating characteristic curve for the priority score was 0.79 and was similar across racial groups. Black patients were more likely than others to be in the lowest priority group (12 [15.2%] vs 34 [8.1%]; P = .046). In an exploratory simulation model using the score for ventilator allocation, with only those in the highest priority group receiving ventilators, there were 43.9% excess deaths among Black patients (18 of 41 patients) and 28.6% (58 of 203 patients among all others (P = .05); when the highest and intermediate priority groups received ventilators, there were 4.9% (2 of 41 patients) excess deaths among Black patients and 3.0% (6 of 203) among all others (P = .53). A random lottery resulted in more excess deaths than the score. Conclusions and Relevance: In this study, a CSOC priority score resulted in lower prioritization of Black patients to receive scarce resources. A model using a random lottery resulted in more estimated excess deaths overall without improving equity by race. CSOC policies must be evaluated for their potential association with racial disparities in health care.
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COVID-19/mortalidade , Etnicidade/estatística & dados numéricos , Alocação de Recursos para a Atenção à Saúde/estatística & dados numéricos , Grupos Raciais/estatística & dados numéricos , Características de Residência/estatística & dados numéricos , Padrão de Cuidado , Idoso , Boston , COVID-19/diagnóstico , COVID-19/terapia , Cuidados Críticos , Feminino , Prioridades em Saúde , Disparidades em Assistência à Saúde , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Escores de Disfunção Orgânica , Estudos Retrospectivos , Índice de Gravidade de Doença , Populações Vulneráveis/estatística & dados numéricosRESUMO
Multiple myeloma is a plasma cell malignancy almost always preceded by precursor conditions, but low tumor burden of these early stages has hindered the study of their molecular programs through bulk sequencing technologies. Here, we generate and analyze single cell RNA-sequencing of plasma cells from 26 patients at varying disease stages and 9 healthy donors. In silico dissection and comparison of normal and transformed plasma cells from the same bone marrow biopsy enables discovery of patient-specific transcriptional changes. Using Non-Negative Matrix Factorization, we discover 15 gene expression signatures which represent transcriptional modules relevant to myeloma biology, and identify a signature that is uniformly lost in abnormal cells across disease stages. Finally, we demonstrate that tumors contain heterogeneous subpopulations expressing distinct transcriptional patterns. Our findings characterize transcriptomic alterations present at the earliest stages of myeloma, providing insight into the molecular underpinnings of disease initiation.
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Mieloma Múltiplo , Humanos , Mieloma Múltiplo/genética , Mieloma Múltiplo/patologia , Carcinogênese/genética , Carcinogênese/patologia , Transformação Celular Neoplásica/patologia , Plasmócitos/patologia , Medula Óssea/patologiaRESUMO
BACKGROUND AND AIMS: Fibroblast growth factor (FGF)19 has been implicated in the pathogenesis of murine hepatocellular carcinoma. Whether it plays a role in the development or course of human cholangiocarcinoma remains to be determined. The aim of this study was to determine whether prolonged exposure to FGF19 results in the transformation of non-malignant human cholangiocytes into cells with malignant features. METHODS: Human SV-40 transfected non-malignant H69 cholangiocytes were cultured with FGF19 (0-50 ng/mL) for 6 weeks, followed by 6 weeks with medium alone. Cell proliferation, invasion, stem cell surface markers, oncofetoprotein expression, state of differentiation, epithelial-mesenchymal transition (EMT) and interleukin (IL)-6 expression were documented at various time intervals throughout the 12-week period. RESULTS: FGF19 exposure was associated with significant increases in cell proliferation, de-differentiation, EMT and IL-6 expression. However, each of these effects returned to baseline or control values during the 6-week FGF19 free follow-up period. The remaining cell properties remained unaltered. CONCLUSIONS: Six weeks of FGF19 exposure did not result in the acquisition of permanent malignant features in non-malignant, human cholangiocytes.
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Advocates have long suggested making shackling incarcerated people during childbirth illegal. Yet exceptions would likely still allow prison personnel to implement restraint and leave clinicians no course for freeing a patient. This article argues that clinicians' assessments of laboring individuals' clinical needs must be prioritized, ethically and legally. This article also explains that, without strong policies in place, some clinicians will not feel empowered to demand that a patient be freed during labor. Beyond prohibiting restraint of laboring individuals, health care organizations must support clinicians seeking to execute their ethical duties to care well and justly for patients. Toward this end, this article proposes a model policy.
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Parto Obstétrico , Parto , Prisioneiros , Restrição Física , Parto Obstétrico/ética , Feminino , Humanos , Gravidez , Prisões/ética , Prisões/legislação & jurisprudência , Restrição Física/éticaRESUMO
Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to subtle choices in study design. We highlight a simple approach, trajectory inspection, to bring clinicians into an iterative design process for model-based RL studies. We identify where the model recommends unexpectedly aggressive treatments or expects surprisingly positive outcomes from its recommendations. Then, we examine clinical trajectories simulated with the learned model and policy alongside the actual hospital course. Applying this approach to recent work on RL for sepsis management, we uncover a model bias towards discharge, a preference for high vasopressor doses that may be linked to small sample sizes, and clinically implausible expectations of discharge without weaning off vasopressors. We hope that iterations of detecting and addressing the issues unearthed by our method will result in RL policies that inspire more confidence in deployment.
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Reforço Psicológico , Sepse , Tomada de Decisão Clínica , Humanos , Aprendizagem , Projetos de PesquisaRESUMO
The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.
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PURPOSE: Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS). METHODS: We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019. Each cohort was divided into training (70%), validation (15%), and test (15%) subsets. Human abstractors identified the clinical rationale associated with treatment discontinuation events. Concatenated EMR notes were used to train high-dimensional logistic regression and convolutional neural network models. Kaplan-Meier analyses were used to compare toxicity incidence and PFS estimated by our NLP models to estimates generated by manual labeling and time-to-treatment discontinuation (TTD). RESULTS: Our best high-dimensional logistic regression models identified toxicity events in early-stage patients with an area under the curve of the receiver-operator characteristic of 0.857 ± 0.014 (standard deviation) and progression events in metastatic patients with an area under the curve of 0.752 ± 0.027 (standard deviation). NLP-extracted toxicity incidence and PFS curves were not significantly different from manually extracted curves (P = .95 and P = .67, respectively). By contrast, TTD overestimated toxicity in early-stage patients (P < .001) and underestimated PFS in metastatic patients (P < .001). Additionally, we tested an extrapolation approach in which 20% of the metastatic cohort were labeled manually, and NLP algorithms were used to abstract the remaining 80%. This extrapolated outcomes approach resolved PFS differences between receptor subtypes (P < .001 for hormone receptor+/human epidermal growth factor receptor 2- v human epidermal growth factor receptor 2+ v triple-negative) that could not be resolved with TTD. CONCLUSION: NLP models are capable of abstracting treatment discontinuation rationale with minimal manual labeling.
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Neoplasias da Mama , Processamento de Linguagem Natural , Algoritmos , Neoplasias da Mama/epidemiologia , Registros Eletrônicos de Saúde , Feminino , Humanos , Estudos RetrospectivosRESUMO
Rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resultant clinical illness, coronavirus disease 2019 (COVID-19), drove the World Health Organization to declare COVID-19 a pandemic. Veno-venous Extra-Corporeal Membrane Oxygenation (VV-ECMO) is an established therapy for management of patients demonstrating the most severe forms of hypoxemic respiratory failure from COVID-19. However, features of COVID-19 pathophysiology and necessary length of treatment present distinct challenges for utilization of VV-ECMO within the current healthcare emergency. In addition, growing allocation concerns due to capacity and cost present significant challenges. Ethical and legal aspects pertinent to triage of this resource-intensive, but potentially life-saving, therapy in the setting of the COVID-19 pandemic are reviewed here. Given considerations relevant to VV-ECMO use, additional emphasis has been placed on emerging hospital resource scarcity and disproportionate representation of healthcare workers among the ill. Considerations are also discussed surrounding withdrawal of VV-ECMO and the role for early communication as well as consultation from palliative care teams and local ethics committees. In discussing how to best manage these issues in the COVID-19 pandemic at present, we identify gaps in the literature and policy important to clinicians as this crisis continues.