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1. Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

   https://doi.org/10.1038/s41598-021-95431-7  

   Su, Chang; Xu, Zhenxing; Hoffman, Katherine; Goyal, Parag; Safford, Monika M.; Lee, Jerry; Alvarez-Mulett, Sergio; Gomez-Escobar, Luis; Price, David R.; Harrington, John S.; et al (December 2021, Scientific Reports)

   null (Ed.)

   Abstract COVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure. 

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2. Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection

   https://doi.org/10.1093/jamia/ocad063  

   Bhavani, Sivasubramanium V; Xiong, Li; Pius, Abish; Semler, Matthew; Qian, Edward T; Verhoef, Philip A; Robichaux, Chad; Coopersmith, Craig M; Churpek, Matthew M (April 2023, Journal of the American Medical Informatics Association)

   Abstract Objective Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes. Materials and Methods Patients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in subphenotype clusters, trajectory patterns, and subphenotype associations with clinical outcomes and treatment responses. Results There were 12 473 patients in training and 8256 patients in validation cohorts. DTW-HC, DTW-PAM, and GBTM models resulted in 4 consistent vitals trajectory patterns with significant agreement in clustering (71–80% agreement, P < .001): group A was hyperthermic, tachycardic, tachypneic, and hypotensive. Group B was hyperthermic, tachycardic, tachypneic, and hypertensive. Groups C and D had lower temperatures, heart rates, and respiratory rates, with group C normotensive and group D hypotensive. Group A had higher odds ratio of 30-day inpatient mortality (P < .01) and group D had significant mortality benefit from balanced crystalloids compared to saline (P < .01) in all 3 models. Discussion DTW- and GBTM-based clustering algorithms applied to vital signs in infected patients identified consistent subphenotypes with distinct clinical outcomes and treatment responses. Conclusion Time series clustering with distinct computational approaches demonstrate similar performance and significant agreement in the resulting subphenotypes. 

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3. 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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4. DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

   https://doi.org/10.1038/s41598-019-38491-0  

   Shickel, Benjamin; Loftus, Tyler J.; Adhikari, Lasith; Ozrazgat-Baslanti, Tezcan; Bihorac, Azra; Rashidi, Parisa (February 2019, Scientific Reports)

   Abstract Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90–0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79–0.80) and 0.85 (95% CI 0.85–0.86). Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization. 

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5. INFORMING INTENSIVE CARE UNIT DIGITAL TWINS: DYNAMIC ASSESSMENT OF CARDIORESPIRATORY FAILURE TRAJECTORIES IN PATIENTS WITH SEPSIS

   https://doi.org/10.1097/SHK.0000000000002536  

   Hou, Grace Yao; Lal, Amos; Schulte, Phillip J; Dong, Yue; Kilickaya, Oguz; Gajic, Ognjen; Zhong, Xiang (January 2025, Shock)

   ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA (P< 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning. 

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