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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. [ABSTRACT FROM AUTHOR]

In this Comment, we provide guidelines for reinforcement learning for decisions about patient treatment that we hope will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner., Author(s): Omer Gottesman [sup.1] , Fredrik Johansson [sup.2] , Matthieu Komorowski [sup.3] [sup.4] , Aldo Faisal [sup.5] [sup.6] [sup.7] [sup.8] , David Sontag [sup.2] , Finale Doshi-Velez [sup.1] , Leo [...]

FGF-16 has been reported to be preferentially expressed in the adult rat heart. We have investigated the expression of FGF-16 in the perinatal and postnatal heart and its functional significance in neonatal rat cardiac myocytes. FGF-16 mRNA accumulation was observed by quantitative RT-PCR between neonatal days 1 and 7, with this increased expression persisting into adulthood. FGF-2 has been shown to increase neonatal rat cardiac myocyte proliferative potential via PKC activation. Gene array analysis revealed that FGF-16 inhibited the upregulation by FGF-2 of cell cycle promoting genes including cyclin F and Ki67. Furthermore, the CDK4/6 inhibitor gene Arf/INK4A was upregulated with the combination of FGF- 16 and FGF-2 but not with either factor on its own. The effect on Ki67 was validated by protein immunodetection, which also showed that FGF-16 significantly decreased FGF-2-induced Ki67 labeling of cardiac myocytes, although it alone had no effect on Ki67 labeling. Inhibition of p38 MAPK potentiated cardiac myocyte proliferation induced by FGF-2 but did not alter the inhibitory action of FGF-16. Receptor binding assay showed that FGF-16 can compete with FGF-2 for binding sites including FGF receptor 1. FGF-16 had no effect on activated p38, ERK1/2, or JNK/SAPK after FGF-2 treatment. However, FGF-16 inhibited PKC-[alpha] and PKC-[member of] activation induced by FGF-2 and, importantly, IGF-1. Collectively, these data suggest that expression and release of FGF-16 in the neonatal myocardium interfere with cardiac myocyte proliferative potential by altering the local signaling environment via modulation of PKC activation and cell cycle-related gene expression. fibroblast growth factor; protein kinase C; Ki67

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. [ABSTRACT FROM AUTHOR]

Protein kinase C (PKC) is activated by contractile agonists and its stimulation leads to changes in the activity of membrane and cytoplasmic proteins. In a study of isolated rabbit portal vein smooth muscle cells, activation of PKC caused a decline in 4-aminopyridine (4-AP)-sensitive delayed rectifier current. Initial exposure of the cells to contractile agonists had no effect on the current but chronic exposure caused a marked depression of the current. The results suggest that PKC catalyzes a novel signal transduction pathway for depressing expression of the current.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Triaging antibiotic use: Use of broad-spectrum, second-line antibiotics in treating urinary tract infections (UTIs) is increasing, likely due to the prevalence of antibiotic resistance. Kanjilal et al. applied a machine learning approach calibrated to local hospital electronic health record data to predict the probability of resistance to first- and second-line antibiotic therapies for uncomplicated UTI. The algorithm then recommended the least broad-spectrum antibiotic to which a given isolate was predicted nonresistant. Use of the pipeline reduced both broad-spectrum and ineffective antibiotic prescription for UTI in the patient cohort relative to clinicians, suggesting the clinical potential of the approach. Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations. [ABSTRACT FROM AUTHOR]

Objective: Most patients with rheumatoid arthritis (RA) strive to consolidate their treatment from methotrexate combinations. The objective of this analysis was to identify patients with RA most likely to achieve remission with tocilizumab (TCZ) monotherapy by developing and validating a prediction model and associated remission score. Methods: We identified four TCZ monotherapy randomized controlled trials in RA and chose two for derivation and two for internal validation. Remission was defined as a Clinical Disease Activity Index score less than 2.8 at 24 weeks post randomization. We used logistic regression to assess the association between each predictor and remission. After selecting variables and assessing model performance in the derivation data set, we assessed model performance in the validation data set. The cohorts were combined to calculate a remission prediction score. Results: The variables selected included younger age, male sex, lower baseline Clinical Disease Activity Index score, shorter RA disease duration, region of the world (Europe and South America [increased odds of remission] versus Asia and North America), no previous exposure to disease‐modifying antirheumatic drugs and/or methotrexate, lower baseline Health Assessment Questionnaire Disability Index score, and baseline hematocrit. The area under the receiver operating characteristic curve was 0.739 in the derivation data set and 0.756 in the validation data set. Patients were categorized into three remission prediction categories based on the remission prediction score: 40% in the low (less than 10% probability of remission), 45% in the intermediate (10%‐25% probability), and 15% in the moderate remission prediction category (greater than 25% probability). Conclusion: We used easily accessible factors to develop a remission prediction score to predict RA remission at 24 weeks after initializing TCZ monotherapy. These results may provide guidance to clinicians tailoring treatment options based on clinical characteristics. [ABSTRACT FROM AUTHOR]

Objective: Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success. In this study, we use supervised machine learning to derive and validate an automated record linkage algorithm between EMS ePCRs and ED records.Materials and Methods: All consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number, and date of birth were extracted. Data were randomly split into 80% training and 20% test datasets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5-fold cross-validation, using label k-fold, L2 regularization, and class reweighting.Results: A total of 14 032 ePCRs were included in the study. Interrater reliability between the primary and secondary reviewer had a kappa of 0.9. The algorithm had a sensitivity of 99.4%, a positive predictive value of 99.9%, and an area under the receiver-operating characteristic curve of 0.99 in both the training and test datasets. Date-of-birth match had the highest odds ratio of 16.9, followed by last name match (10.6). Social security number match had an odds ratio of 3.8.Conclusions: We were able to successfully derive and validate a record linkage algorithm from a single EMS ePCR provider to our hospital EMR. [ABSTRACT FROM AUTHOR]

Farah Sheikh, David P. Sontag, Robert R. Fandrich, Elissavet Kardami, and Peter A. Cattini Overexpression of FGF-2 increases cardiac myocyte viability after injury in isolated mouse hearts. Am J Physiol Heart Circ Physiol 280: H1039-H1050, 2001.--We generated transgenic (TG) mice overexpressing fibroblast growth factor (FGF)-2 protein (22- to 34-fold) in the heart. Chronic FGF-2 overexpression revealed no significant effect on heart weight-to-body weight ratio or expression of cardiac differentiation markers. There was, however, a significant 20% increase in capillary density. Although there was no change in FGF receptor-1 expression, relative levels of phosphorylated c-Jun [NH.sub.2]-terminal kinase and p38 kinase as well as of membrane-associated protein kinase C (PKC)-[Alpha] and total PKC-[element of] were increased in FGF-2-TG mouse hearts. An isolated mouse heart model of ischemia-reperfusion injury was used to assess the potential of increased endogenous FGF-2 for cardioprotection. A significant 34-45% increase in myocyte viability, reflected in a decrease in lactate dehydrogenase released into the perfusate, was observed in FGF-2 overexpressing mice and non-TG mice treated exogenously with FGF-2. In conclusion, FGF-2 overexpression causes augmentation of signal transduction pathways and increased resistance to ischemic injury. Thus, stimulation of endogenous FGF-2 expression offers a potential mechanism to enhance cardioprotection. fibroblast growth factor; transgenic mice; cardioprotection; Langendorff preparation

Objective: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department. Methods: This was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model. Results: A total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65–0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81–0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85–0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84–0.86) for the test data set. Conclusion: Compared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient's electronic medical record phenotype will play an important role in triggering clinical decision support systems that can deliver personalized recommendations in real time. Learning with anchors presents a method of efficiently learning statistically driven phenotypes with minimal manual intervention.Materials and Methods: We developed a phenotype library that uses both structured and unstructured data from the EMR to represent patients for real-time clinical decision support. Eight of the phenotypes were evaluated using retrospective EMR data on emergency department patients using a set of prospectively gathered gold standard labels.Results: We built a phenotype library with 42 publicly available phenotype definitions. Using information from triage time, the phenotype classifiers have an area under the ROC curve (AUC) of infection 0.89, cancer 0.88, immunosuppressed 0.85, septic shock 0.93, nursing home 0.87, anticoagulated 0.83, cardiac etiology 0.89, and pneumonia 0.90. Using information available at the time of disposition from the emergency department, the AUC values are infection 0.91, cancer 0.95, immunosuppressed 0.90, septic shock 0.97, nursing home 0.91, anticoagulated 0.94, cardiac etiology 0.92, and pneumonia 0.97.Discussion: The resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients.Conclusion: Learning with anchors is an attractive option for building a large public repository of phenotype definitions that can be used for a range of health IT applications, including real-time decision support. [ABSTRACT FROM AUTHOR]

To improve provider knowledge of the construct of capacity, clarify existing capacity assessment institutional policies, and provide recommendations for conducting capacity assessment. A semi-structured interview script and Likert scale survey were developed to investigate stakeholder knowledge and perspectives on decision-making capacity. Constant Comparative Method Analysis was conducted to identify themes across and within stakeholder groups. Inpatient Rehabilitation Hospital. Multidisciplinary stakeholder representatives, including physicians, investigators, hospital administrators, legal counsel, psychologists, case managers, nurses, social workers, caregivers, and patients (N=21), selected by the investigators using purposive sampling. Not Applicable. Four thematic areas (i.e. clinical, research, ethical, legal) identified through application of a data reduction procedure. Ethically, capacity assessment promotes patients' autonomy, dignity, best interests, well-being, values, preferences and goals of care, and allows patients to maintain the right to refuse treatment. Clinically, stakeholders reported that capacity assessment ensures that patient preferences are considered in the plan of care and that unwanted procedures and treatments are prevented. Legally, capacity assessment ensures there is valid informed consent. For research, capacity assessment helps establish a point person to make decisions and allows for decisions to be made regarding patient participation in research studies. Providers identified challenges including lack of time, lack of familiarity with legal statutes, and uncertainty about the multidimensionality of capacity. Investigators identified challenges including tracking proxy/guardian status long-term and lack of consensus between clinical and research teams. Surrogate decision makers identified challenges including lack of direct contact with patients, family discord, and negative impact of personal emotions. For healthcare systems, challenges included incomplete medical records, high staff turnover, and team miscommunication. A provider toolkit for capacity assessment and monitoring was developed based on challenges identified by stakeholder groups and recommendations were proposed for refining institutional policy and procedures. While stakeholder groups appear to have a general understanding of the concept of capacity, familiarity with specific procedures and regulations is limited. Institutions should provide supportive resources for providers directly involved in assessment of decision-making capacity. No Disclosures. [ABSTRACT FROM AUTHOR]

Authors of scientific articles and journal editors are subject to antidefamation publication laws. We describe our experience with an accusation of libel. We define libel as it involves the medical literature and explain the ways in which threats of libel influence editorial decisionmaking and lead to negative publication bias by presenting examples drawn from the medical and legal literature. [Copyright &y& Elsevier]

FGF-16 has been reported to be preferentially expressed in the adult rat heart. We have investigated the expression of FGF-16 in the perinatal and postnatal heart and its functional significance in neonatal rat cardiac myocytes. FGF-16 mRNA accumulation was observed by quantitative RT-PCR between neonatal days / and 7, with this increased expression persisting into adulthood. FGF-2 has been shown to increase neonatal rat cardiac myocyte proliferative potential via PKC activation. Gene array anal- ysis revealed that FGF- 16 inhibited the upregulation by FGF-2 of cell cycle promoting genes including cyclin F and Ki67. Furthermore, the CDK4I6 inhibitor gene Arf/INK4A was upregulated with the combi- nation of FGF- 16 and FGF-2 but not with either factor on its own. The effect on Ki67 was validated by protein immunodetection, which also showed that FGF- 16 significantly decreased FGF-2-induced Ki67 labeling of cardiac myocytes, although it alone had no effect on Ki67 labeling. Inhibition of p38 MAPK potentiated cardiac myocyte pro- liferation induced by FGF-2 but did not alter the inhibitory action of FGF-16. Receptor binding assay showed that FGF-l6 can compete with FGF-2 for binding sites including FGF receptor I. FGF- 16 had no effect on activated p38, ERK1/2, or JNK/SAPK after FGF-2 treatment. However, FGF-16 inhibited PKC-a and PKC-c activation induced by FGF-2 and, importantly, IGF-l. Collectively, these data suggest that expression and release of FGF-16 in the neonatal myo- cardium interfere with cardiac myocyte proliferative potential by altering the local signaling environment via modulation of PKC activation and cell cycle-related gene expression. [ABSTRACT FROM AUTHOR]

This article addresses all of the issues involved in the debate about whether or not bioethicists should be paid by private biomedical companies to perform consultations. These issues include the following: differentiation of this role from bioethicists' other roles, an analysis of to whom bioethicists owe a duty, consideration of what bioethicists are “selling,” whether bioethicists should be allowed to get paid, when payment becomes problematic, and whether consulting fee arrangements should be regulated. The author often compares bioethicists' relationship to the companies to bioethicists' other relationships, as well as to professional relationships in other fields, such as law and accounting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Practitioners in diverse felds such as healthcare, economics and education are eager to apply machine learning to improve decision making. The cost and impracticality of performing experiments and a recent monumental increase in electronic record keeping has brought attention to the problem of evaluating decisions based on non-experimental observational data. This is the setting of this work. In particular, we study estimation of individual-level potential outcomes and causal effects|such as a single patient's response to alternative medication|from recorded contexts, decisions and outcomes. We give generalization bounds on the error in estimated outcomes based on distributional distance measures between re-weighted samples of groups receiving different treatments. We provide conditions under which our bounds are tight and show how they relate to results for unsupervised domain adaptation. Led by our theoretical results, we devise algorithms which learn representations and weighting functions that minimize our bounds by regularizing the representation's induced treatment group distance, and encourage sharing of information between treatment groups. Finally, an experimental evaluation on real and synthetic data shows the value of our proposed representation architecture and regularization scheme. [ABSTRACT FROM AUTHOR]

Structured prediction is used in areas including computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation for the striking observation that approximations based on linear programming (LP) relaxations are often tight (exact) on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that this training tightness generalizes to test data. [ABSTRACT FROM AUTHOR]