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1. 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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2. Ratchet recruitment in the acute respiratory distress syndrome: lessons from the newborn cry

   https://doi.org/10.3389/fphys.2023.1287416  

   Nieman, Gary F.; Herrmann, Jacob; Satalin, Joshua; Kollisch-Singule, Michaela; Andrews, Penny L.; Habashi, Nader M.; Tingay, David G.; Gaver, Donald P.; Bates, Jason H.; Kaczka, David W. (November 2023, Frontiers in Physiology)

   Alysson Roncally Silva Carvalho (Ed.)

   Patients with acute respiratory distress syndrome (ARDS) have few treatment options other than supportive mechanical ventilation. The mortality associated with ARDS remains unacceptably high, and mechanical ventilation itself has the potential to increase mortality further by unintended ventilator-induced lung injury (VILI). Thus, there is motivation to improve management of ventilation in patients with ARDS. The immediate goal of mechanical ventilation in ARDS should be to prevent atelectrauma resulting from repetitive alveolar collapse and reopening. However, a long-term goal should be to re-open collapsed and edematous regions of the lung and reduce regions of high mechanical stress that lead to regional volutrauma. In this paper, we consider the proposed strategy used by the full-term newborn to open the fluid-filled lung during the initial breaths of life, by ratcheting tissues opened over a series of initial breaths with brief expirations. The newborn’s cry after birth shares key similarities with the Airway Pressure Release Ventilation (APRV) modality, in which the expiratory duration is sufficiently short to minimize end-expiratory derecruitment. Using a simple computational model of the injured lung, we demonstrate that APRV can slowly open even the most recalcitrant alveoli with extended periods of high inspiratory pressure, while reducing alveolar re-collapse with brief expirations. These processes together comprise a ratchet mechanism by which the lung is progressively recruited, similar to the manner in which the newborn lung is aerated during a series of cries, albeit over longer time scales. 

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3. Impact of Obesity on Timing of Tracheotomy: A Multi‐institutional Retrospective Study

   https://doi.org/10.1002/lary.31586  

   Yousef, Andrew; Soliman, Shady I; Solomon, Isaac; Panuganti, Bharat A; Francis, David O; Pang, John; Klebaner, Dasha; Asturias, Alicia; Alattar, Ali; Wood, Samuel; et al (November 2024, The Laryngoscope)

   ObjectiveTo examine the impact of increased body mass index (BMI) on (1) tracheotomy timing and (2) short‐term surgical complications requiring a return to the operating room and 30‐day mortality utilizing data from the Multi‐Institutional Study on Tracheotomy (MIST). MethodsA retrospective analysis of patients from the MIST database who underwent surgical or percutaneous tracheotomy between 2013 and 2016 at eight institutions was completed. Unadjusted and adjusted logistic regression analyses were used to assess the impact of obesity on tracheotomy timing and complications. ResultsAmong the 3369 patients who underwent tracheotomy, 41.0% were obese and 21.6% were morbidly obese. BMI was associated with higher rates of prolonged intubation prior to tracheotomy accounting for comorbidities, indication for tracheotomy, institution, and type of tracheostomy (p = 0.001). Morbidly obese patients (BMI ≥35 kg/m²) experienced a longer duration of intubation compared with patients with a normal BMI (median days intubated [IQR 25%–75%]: 11.0 days [7–17 days] versus 9.0 days [5–14 days];p < 0.001) but did not have statistically higher rates of return to the operating room within 30 days (p = 0.12) or mortality (p = 0.90) on multivariable analysis. This same finding of prolonged intubation was not seen in overweight, nonobese patients when compared with normal BMI patients (median days intubated [IQR 25%–75%]: 10.0 days [6–15 days] versus 10.0 days [6–15 days];p = 0.36). ConclusionBMI was associated with increased duration of intubation prior to tracheotomy. Although morbidly obese patients had a longer duration of intubation, there were no differences in return to the operating room or mortality within 30 days. Level of Evidence3Laryngoscope, 134:4674–4681, 2024 

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4. Rule-Based Cohort Definitions for Acute Respiratory Failure: Electronic Phenotyping Algorithm

   https://doi.org/10.2196/18402  

   Essay, Patrick; Mosier, Jarrod; Subbian, Vignesh (January 2020, JMIR Medical Informatics)

   Background Acute respiratory failure is generally treated with invasive mechanical ventilation or noninvasive respiratory support strategies. The efficacies of the various strategies are not fully understood. There is a need for accurate therapy-based phenotyping for secondary analyses of electronic health record data to answer research questions regarding respiratory management and outcomes with each strategy. Objective The objective of this study was to address knowledge gaps related to ventilation therapy strategies across diverse patient populations by developing an algorithm for accurate identification of patients with acute respiratory failure. To accomplish this objective, our goal was to develop rule-based computable phenotypes for patients with acute respiratory failure using remotely monitored intensive care unit (tele-ICU) data. This approach permits analyses by ventilation strategy across broad patient populations of interest with the ability to sub-phenotype as research questions require. Methods Tele-ICU data from ≥200 hospitals were used to create a rule-based algorithm for phenotyping patients with acute respiratory failure, defined as an adult patient requiring invasive mechanical ventilation or a noninvasive strategy. The dataset spans a wide range of hospitals and ICU types across all US regions. Structured clinical data, including ventilation therapy start and stop times, medication records, and nurse and respiratory therapy charts, were used to define clinical phenotypes. All adult patients of any diagnoses with record of ventilation therapy were included. Patients were categorized by ventilation type, and analysis of event sequences using record timestamps defined each phenotype. Manual validation was performed on 5% of patients in each phenotype. Results We developed 7 phenotypes: (0) invasive mechanical ventilation, (1) noninvasive positive-pressure ventilation, (2) high-flow nasal insufflation, (3) noninvasive positive-pressure ventilation subsequently requiring intubation, (4) high-flow nasal insufflation subsequently requiring intubation, (5) invasive mechanical ventilation with extubation to noninvasive positive-pressure ventilation, and (6) invasive mechanical ventilation with extubation to high-flow nasal insufflation. A total of 27,734 patients met our phenotype criteria and were categorized into these ventilation subgroups. Manual validation of a random selection of 5% of records from each phenotype resulted in a total accuracy of 88% and a precision and recall of 0.8789 and 0.8785, respectively, across all phenotypes. Individual phenotype validation showed that the algorithm categorizes patients particularly well but has challenges with patients that require ≥2 management strategies. Conclusions Our proposed computable phenotyping algorithm for patients with acute respiratory failure effectively identifies patients for therapy-focused research regardless of admission diagnosis or comorbidities and allows for management strategy comparisons across populations of interest. 

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5. Early prediction of level-of-care requirements in patients with COVID-19

   https://doi.org/10.7554/eLife.60519  

   Hao, Boran; Sotudian, Shahabeddin; Wang, Taiyao; Xu, Tingting; Hu, Yang; Gaitanidis, Apostolos; Breen, Kerry; Velmahos, George C; Paschalidis, Ioannis Ch (October 2020, eLife)

   null (Ed.)

   The new coronavirus (now named SARS-CoV-2) causing the disease pandemic in 2019 (COVID-19), has so far infected over 35 million people worldwide and killed more than 1 million. Most people with COVID-19 have no symptoms or only mild symptoms. But some become seriously ill and need hospitalization. The sickest are admitted to an Intensive Care Unit (ICU) and may need mechanical ventilation to help them breath. Being able to predict which patients with COVID-19 will become severely ill could help hospitals around the world manage the huge influx of patients caused by the pandemic and save lives. Now, Hao, Sotudian, Wang, Xu et al. show that computer models using artificial intelligence technology can help predict which COVID-19 patients will be hospitalized, admitted to the ICU, or need mechanical ventilation. Using data of 2,566 COVID-19 patients from five Massachusetts hospitals, Hao et al. created three separate models that can predict hospitalization, ICU admission, and the need for mechanical ventilation with more than 86% accuracy, based on patient characteristics, clinical symptoms, laboratory results and chest x-rays. Hao et al. found that the patients’ vital signs, age, obesity, difficulty breathing, and underlying diseases like diabetes, were the strongest predictors of the need for hospitalization. Being male, having diabetes, cloudy chest x-rays, and certain laboratory results were the most important risk factors for intensive care treatment and mechanical ventilation. Laboratory results suggesting tissue damage, severe inflammation or oxygen deprivation in the body's tissues were important warning signs of severe disease. The results provide a more detailed picture of the patients who are likely to suffer from severe forms of COVID-19. Using the predictive models may help physicians identify patients who appear okay but need closer monitoring and more aggressive treatment. The models may also help policy makers decide who needs workplace accommodations such as being allowed to work from home, which individuals may benefit from more frequent testing, and who should be prioritized for vaccination when a vaccine becomes available. 

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