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1. Physiologic signatures within six hours of hospitalization identify acute illness phenotypes

   https://doi.org/10.1371/journal.pdig.0000110  

   Ren, Yuanfang; Loftus, Tyler J.; Li, Yanjun; Guan, Ziyuan; Ruppert, Matthew M.; Datta, Shounak; Upchurch, Gilbert R.; Tighe, Patrick J.; Rashidi, Parisa; Shickel, Benjamin; et al (October 2022, PLOS Digital Health)

   Keim-Malpass, Jessica (Ed.)

   During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiological signatures and clinical outcomes. We created a longitudinal electronic health record dataset for 75,762 adult patient admissions to a tertiary care center in 2014–2016 lasting six hours or longer. Physiotypes were derived via unsupervised machine learning in a training cohort of 41,502 patients applying consensus k -means clustering to six vital signs measured within six hours of admission. Reproducibility and correlation with clinical biomarkers and outcomes were assessed in validation cohort of 17,415 patients and testing cohort of 16,845 patients. Training, validation, and testing cohorts had similar age (54–55 years) and sex (55% female), distributions. There were four distinct clusters. Physiotype A had physiologic signals consistent with early vasoplegia, hypothermia, and low-grade inflammation and favorable short-and long-term clinical outcomes despite early, severe illness. Physiotype B exhibited early tachycardia, tachypnea, and hypoxemia followed by the highest incidence of prolonged respiratory insufficiency, sepsis, acute kidney injury, and short- and long-term mortality. Physiotype C had minimal early physiological derangement and favorable clinical outcomes. Physiotype D had the greatest prevalence of chronic cardiovascular and kidney disease, presented with severely elevated blood pressure, and had good short-term outcomes but suffered increased 3-year mortality. Comparing sequential organ failure assessment (SOFA) scores across physiotypes demonstrated that clustering did not simply recapitulate previously established acuity assessments. In a heterogeneous cohort of hospitalized patients, unsupervised machine learning techniques applied to routine, early vital sign data identified physiotypes with unique disease categories and distinct clinical outcomes. This approach has the potential to augment understanding of pathophysiology by distilling thousands of disease states into a few physiological signatures. 

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2. Network-Based Modeling of Sepsis: Quantification and Evaluation of Simultaneity of Organ Dysfunctions

   https://doi.org/10.1145/3307339.3342160  

   Jazayeri, Ali; Capan, Muge; Yang, Christopher; Khoshnevisan, Farzaneh; Chi, Min; Arnold, Ryan (January 2019, Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics 2019)

   It is shown that appropriate therapeutic management at early stages of sepsis are crucial for preventing further deterioration and irreversible organ damage. Although previous studies considered the cellular and physiological responses as the components of sepsis-related predictive models, temporal connections among the responses have not been widely studied. The objective of this study is to investigate simultaneous changes in cellular and physiological responses represented by 16 clinical variables contributing to seven organ system dysfunctions in patients with sepsis to predict in-hospital mortality. Organ dysfunctions were represented by undirected weighted network models composed of: i) nodes (i.e., 16 clinical variables and three biomarkers including procalcitonin, C-reactive protein, and sedimentation rate), ii) edges (i.e., connection between pair of nodes representing simultaneous dysfunctions), and iii) weights representing the persistence of the co-occurrence of two dysfunctions. Data was collected from 13,367 adult patients (corresponding to 17,953 visits) admitted to the study hospital from July 1, 2013, to December 31, 2015. The study population were categorized based on clinical criteria representing sepsis progression to identify different subpopulations. The findings quantify the optimal window for defining the simultaneity of two dysfunctions, the network properties corresponding to different subpopulations, the discriminatory patterns of simultaneous dysfunctions among subpopulations and in-hospital mortality prediction. The results show that the level of persistence of simultaneous dysfunctions are subpopulation-specific. Insights from this study regarding optimal thresholds of the persistence and combination of simultaneous organ dysfunctions can inform policies to personalize the in-hospital mortality prediction. 

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3. DeepAISE on FHIR — An Interoperable Real-Time Predictive Analytic Platform for Early Prediction of Sepsis

   Lakshman, Vidyashankar; Amrollahi, Fatemeh; Koppisetty, Veera Supraja; Shashikumar, Supreeth P.; Sharma, Ashish; Nemati, Shamim (October 2018, AMIA Annual Symposium proceedings)

   Sepsis, a dysregulated immune-mediated host response to infection, is lethal, prevalent, and costly. It’s early detection has the potential to drastically reduce morbidity/mortality. We have developed a real-time cloud-based application that predicts onset-time of sepsis based on live ICU data and provides clinicians with actionable visual alerts. Clinicians and nurses can examine these alerts and initiate appropriate interventions. The prediction engine (DeepAISE) is a Deep Learning-based algorithm trained to reliably predict sepsis 4-6 hours in advance of clinical recognition. A scalable, cloud-based, system continuously streams bedside data and uses the prediction engine to generate hourly scores and displays these to clinicians. Interoperability is achieved through the use of FHIR resources and APIs. This system is monitoring ~100 patients on a daily basis at the Emory Tele-ICU center, and has been shown to reliably predict onset of sepsis with an AUC of 0.9. 

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4. Sepsis subphenotyping based on organ dysfunction trajectory

   https://doi.org/10.1186/s13054-022-04071-4  

   Xu, Zhenxing; Mao, Chengsheng; Su, Chang; Zhang, Hao; Siempos, Ilias; Torres, Lisa K.; Pan, Di; Luo, Yuan; Schenck, Edward J.; Wang, Fei (December 2022, Critical Care)

   Abstract Background Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis. Methods We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies. Results A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening ( n  = 612, 13.1%), Delayed Worsening ( n  = 960, 20.5%), Rapidly Improving ( n  = 1932, 41.3%), and Delayed Improving ( n  = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P -value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers. Conclusions Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials. 

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5. A multi-instance support vector machine with incomplete data for clinical outcome prediction of COVID-19

   https://doi.org/10.1145/3459930.3469552  

   Brand, Lodewijk; Baker, Lauren Zoe; Wang, Hua (August 2021, Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health)

   null (Ed.)

   In order to manage the public health crisis associated with COVID-19, it is critically important that healthcare workers can quickly identify high-risk patients in order to provide effective treatment with limited resources. Statistical learning tools have the potential to help predict serious infection early-on in the progression of the disease. However, many of these techniques are unable to take full advantage of temporal data on a per-patient basis as they handle the problem as a single-instance classification. Furthermore, these algorithms rely on complete data to make their predictions. In this work, we present a novel approach to handle the temporal and missing data problems, simultaneously; our proposed Simultaneous Imputation-Multi Instance Support Vector Machine method illustrates how multiple instance learning techniques and low-rank data imputation can be utilized to accurately predict clinical outcomes of COVID-19 patients. We compare our approach against recent methods used to predict outcomes on a public dataset with a cohort of 361 COVID-19 positive patients. In addition to improved prediction performance early on in the progression of the disease, our method identifies a collection of biomarkers associated with the liver, immune system, and blood, that deserve additional study and may provide additional insight into causes of patient mortality due to COVID-19. We publish the source code for our method online. 

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