Harnessing Machine Learning to Improve Patient Outcomes in Pulmonary and Critical Care Medicine.
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ABSTRACT:
SUBMITTER: Levenson AR
PROVIDER: S-EPMC7528791 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
Harnessing Machine Learning to Improve Patient Outcomes in Pulmonary and Critical Care Medicine.
Levenson Anne R AR Morales-Nebreda Luisa L Alexander Michael J MJ Schroedl Clara J CJ
American journal of respiratory and critical care medicine 20201001 7
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Project description:Patient-centered outcomes research (PCOR) represents a paradigm shift in research methods aimed to create the body of evidence that supports clinical practice and informs health care decisions. PCOR integrates patients and other key stakeholders including family members, policy makers, clinicians, and patient advocates and advocacy groups as research partners throughout all stages of the research process. The importance of PCOR has received increased recognition, yet there is little evidence available to help guide researchers interested in the design and conduct of PCOR. In May 2014, we convened a workshop to identify key issues related to designing, conducting, and disseminating findings from PCOR studies. Workshop participants included a diverse group of patients, patient advocates, clinicians (physicians, nurses, psychologists, and advanced practice providers), researchers, administrators, and funders within and beyond the pulmonary, critical care, and sleep medicine communities. Participants identified important issues and considerations to address when undertaking PCOR. In this report, we summarize the results of this workshop to inform members of the pulmonary, sleep, and critical care community interested in participating in PCOR. Key findings include the following: 1) requirements for research to be considered PCOR; 2) the potential significant impact of PCOR on patients, clinicians, and researchers; 3) guiding principles and practical strategies to form successful patient-centered research partnerships, conduct PCOR, and disseminate study results to a broad audience of stakeholders; 4) benefits and challenges of PCOR for researchers; and 5) resources available within the American Thoracic Society to help with the conduct of PCOR.
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Project description:Background: Little is known about historical and recent application trends for pulmonary critical care medicine (PCCM) or pulmonary medicine (PM) fellowship programs. Describing trends in and characteristics of PCCM and PM applications, applicants, and fellowship programs can help program directors and medical educators understand trainees' interest in and application patterns for these fellowship programs. Objective: The objective of this study was to use National Residency Match Program data to assess recent trends in PCCM and PM fellowship applications and compare characteristics of applicants and fellowship programs. Methods: In 2019, we used National Residency Match Program data to evaluate applicant ranking and matching in PCCM and PM fellowship programs and to compare applicant and fellowship program characteristics. Results: From 2008 through 2019, the majority of applicants (59.1%) matched into PCCM were graduates of U.S. allopathic or osteopathic medical schools, whereas 87% of PM fellows were non-U.S. graduates. PCCM was the preferred specialty for 90.8% of matched applicants versus only 31.6% of matched PM applicants (P < 0.001). The match rate for PCCM applicants was 67.2% versus 23.8% for PM applicants (P < 0.001). Of PCCM applicants, 36.6% matched into their top choice versus 10.8% of PM applicants (P < 0.001). There are far fewer PM fellowship positions (n = 23) and programs (n = 12) than PCCM positions (n = 450) and programs (n = 131). The mean fill rates from the 2004 through 2016 appointment years are 94.1% in PCCM and 97.4% in PM (P = 0.009). Conclusion: PCCM is a prevailing specialty choice over PM among residency graduates, with matched applicants more likely to list PCCM than PM as their preferred specialty. Further exploration into applicants' interest in critical care compared with PM may prove beneficial in guiding applicants to programs that will best meet their career goals.
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Project description:Introduction The mastery of mechanical ventilation (MV) management is challenging, as it requires the integration of physiological and technological knowledge with critical thinking. Our aim was to create a standardized curriculum with assessment tools based on evidence-based practices to identify the skill deficit and improve knowledge in MV management. Methods For 3 years, 3 hours of standardized curriculum for each first-year pulmonary critical care medicine (PCCM) and critical care medicine (CCM) fellows was integrated into the orientation (chronologically): (1) a baseline knowledge pretest; (2) a 1-hour one-on-one case-based simulation session with debriefing. A 34-item competency checklist was used to assess critically thinking and skills and guide the debriefing; (3) a 1-hour group didactic on respiratory mechanics and physiology; (4) a 45-minute hands-on session in small groups of one to three fellows for basic knobology, waveforms, and various modes of mechanical ventilators; (5) a 15-minute group bedside teaching of vented patients covering topics such as techniques to alleviate dyssynchrony and advanced ventilator modes; (6) a one-on-one simulation reassessment session; (7) a knowledge posttest. Fellows' performances at baseline, 1-month posttest, and end-of-first year post-test were compared. Results Fellows ( n = 24) demonstrated significant improvement at 1-month posttest in knowledge (54.2% ± 11.0 vs. 76.6 ± 11.7%, p < 0.001) and MV competency (40.7 ± 11.0% vs. 69.7 ± 9.3%, p < 0.001), compared with pretest. These improvements were retained at the end-of-year reassessments (knowledge 75.1 ± 14.5% and MV competency 85.5 ± 8.7%; p < 0.001). Conclusion Standardized simulation-based MV curriculum may improve the medical knowledge competency, and confidence of first-year PCCM and CCM fellows toward MV management before encountering actual ventilated patients.
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Project description:BACKGROUND:Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. RESULTS:The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. CONCLUSION:We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.
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Project description:BackgroundEndotracheal intubation in the intensive care unit (ICU) is a high-risk procedure. Competence in endotracheal intubation is a requirement for Pulmonary and Critical Care Medicine (PCCM) training programs, but fellow experience as the primary operator in intubating ICU patients has not been described on a large scale.ObjectiveWe hypothesized that significant variation surrounding endotracheal intubation practices in medical ICUs exists in United States (US) PCCM training programs.MethodsWe administered a survey to a convenience sample of US PCCM fellows to elicit typical intubation practices in the medical ICU.Results89 discrete US PCCM and Internal Medicine CCM training programs (77% response rate) were represented. At 43% of programs, the PCCM fellow was "always or almost always" designated the primary operator for intubation of a medical ICU patient, whereas at 21% of programs, the PCCM fellow was "rarely or never" the primary operator responsible for intubating in the ICU. Factors influencing this variation included time of day, hospital policies, attending skill or preference, ICU census and acuity, and patient factors. There was an association between location of the training program, but not program size, and whether the PCCM fellow was the primary operator.ConclusionThere is significant variation in whether PCCM fellows are the primary operators to intubate medical ICU patients during training. Further work should explore how this variation affects fellow career development and competence in intubation.
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Project description:Degradation of intracellular proteins in Gram-negative bacteria regulates various cellular processes and serves as a quality control mechanism by eliminating damaged proteins. To understand what causes the proteolytic machinery of the cell to degrade some proteins while sparing others, we employed a quantitative pulsed-SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method followed by mass spectrometry analysis to determine the half-lives for the proteome of exponentially growing Escherichia coli, under standard conditions. We developed a likelihood-based statistical test to findactively degraded proteins, and identified dozens of novel proteins that are fast-degrading. Finally, we used structural, physicochemical and protein-protein interaction network descriptorsto train a machine-learning classifier to discriminate fast-degrading proteins from the rest of the proteome. Our combined computational-experimental approach provides means for proteomic-based discovery of fast degrading proteins in bacteria and the elucidation of the factors determining protein half-livesand have implications for protein engineering. Moreover, as rapidly degraded proteins may play an important role in pathogenesis, our findings could identify new potential antibacterial drug targets
2021-01-13 | PXD022112 | Pride
Project description:Purpose: Competency assessment standards for Critical Care Ultrasonography (CCUS) for Graduate Medical Education (GME) trainees in pulmonary/critical care medicine (PCCM) fellowship programs are lacking. We sought to answer the following research questions: How are PCCM fellows and teaching faculty assessed for CCUS competency? Which CCUS teaching methods are perceived as most effective by program directors (PDs) and fellows. Methods: Cross-sectional, nationwide, electronic survey of PCCM PDs and fellows in accredited GME training programs. Results: PDs and fellows both reported the highest rates of fellow competence to use CCUS for invasive procedural guidance, but lower rates for assessment of deep vein thrombosis and abdominal organs. 54% and 90% of PDs reported never assessing fellows or teaching faculty for CCUS competency, respectively. PDs and fellows perceived hands-on workshops and directly supervised CCUS exams as more effective learning methods than unsupervised CCUS archival with subsequent review and self-directed learning. Conclusions: There is substantial variation in CCUS competency assessment among PCCM fellows and teaching faculty nationwide. The majority of training programs do not formally assess fellows or teaching faculty for CCUS competence. Guidelines are needed to formulate standardized competency assessment tools for PCCM fellowship programs.
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