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1. Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach

   https://doi.org/10.1213/ANE.0000000000006753  

   Laferrière-Langlois, Pascal ; Imrie, Fergus ; Geraldo, Marc-Andre ; Wingert, Theodora ; Lahrichi, Nadia ; van der Schaar, Mihaela ; Cannesson, Maxime ( December 2023 , Anesthesia & Analgesia)

   Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS).

   We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensusk-means clustering was performed independently on each derivation cohort, from which phenotypes’ characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score.

   A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71).

   For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.

    

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2. 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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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. Performance evaluation and validation of the Animal Trauma Triage score and modified Glasgow Coma Scale in injured cats: A Veterinary Committee on Trauma registry study

   https://doi.org/10.1111/vec.12885  

   Lapsley, Janis ; Hayes, Galina M. ; Sumner, Julia P. ( August 2019 , Journal of Veterinary Emergency and Critical Care)

   To examine the Animal Trauma Triage (ATT) and modified Glasgow Coma Scale (mGCS) scores as predictors of mortality in injured cats.

   Observational cohort study conducted September 2013 to March 2015.

   Nine Level I and II veterinary trauma centers.

   Consecutive sample of 711 cats reported on the Veterinary Committee on Trauma (VetCOT) case registry.

   None.

   We compared the predictive power (area under receiver operating characteristic curve; AUROC) and calibration of the ATT and mGCS scores to their components. Overall mortality risk was 16.5% (95% confidence interval [CI], 13.9‐19.4). Head trauma prevalence was 11.8% (n = 84). The ATT score showed a linear relationship with mortality risk. Discriminatory performance of the ATT score was excellent (AUROC = 0.87 [95% CI, 0.84‐0.90]). Each ATT score increase of 1 point was associated with an increase in mortality odds of 1.78 (95% CI, 1.61‐1.97,P < 0.001). The eye/muscle/integument category of the ATT showed the lowest discrimination (AUROC = 0.60). When this component, skeletal, and cardiac components were omitted from score calculation, there was no loss in discriminatory capacity compared with the full score (AUROC = 0.86 vs 0.87, respectively,P= 0.66). The mGCS showed fair performance overall for prediction of mortality, but the point estimate of performance improved when restricted to head trauma patients (AUROC = 0.75, 95% CI, 0.70‐0.80 vs AUROC = 0.80, 95% CI, 0.70‐0.90). The motor component of the mGCS showed the best predictive performance (AUROC = 0.71); however, the full score performed better than the motor component alone (P= 0.004). When assessment was restricted to patients with head injury (n = 84), there was no difference in performance between the ATT and mGCS scores (AUROC = 0.82 vs 0.80,P= 0.67).

   On a large, multicenter dataset of feline trauma patients, the ATT score showed excellent discrimination and calibration for predicting mortality; however, an abbreviated score calculated from the perfusion, respiratory, and neurologic categories showed equivalent performance.

    

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5. Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points

   https://doi.org/10.1001/jamanetworkopen.2023.15250  

   Lin, Yannan ; Liang, Li-Jung ; Ding, Ruiwen ; Prosper, Ashley Elizabeth ; Aberle, Denise R. ; Hsu, William ( May 2023 , JAMA Network Open)

   Screening with low-dose computed tomography (CT) has been shown to reduce mortality from lung cancer in randomized clinical trials in which the rate of adherence to follow-up recommendations was over 90%; however, adherence to Lung Computed Tomography Screening Reporting &amp; Data System (Lung-RADS) recommendations has been low in practice. Identifying patients who are at risk of being nonadherent to screening recommendations may enable personalized outreach to improve overall screening adherence.

   To identify factors associated with patient nonadherence to Lung-RADS recommendations across multiple screening time points.

   This cohort study was conducted at a single US academic medical center across 10 geographically distributed sites where lung cancer screening is offered. The study enrolled individuals who underwent low-dose CT screening for lung cancer between July 31, 2013, and November 30, 2021.

   Low-dose CT screening for lung cancer.

   The main outcome was nonadherence to follow-up recommendations for lung cancer screening, defined as failing to complete a recommended or more invasive follow-up examination (ie, diagnostic dose CT, positron emission tomography–CT, or tissue sampling vs low-dose CT) within 15 months (Lung-RADS score, 1 or 2), 9 months (Lung-RADS score, 3), 5 months (Lung-RADS score, 4A), or 3 months (Lung-RADS score, 4B/X). Multivariable logistic regression was used to identify factors associated with patient nonadherence to baseline Lung-RADS recommendations. A generalized estimating equations model was used to assess whether the pattern of longitudinal Lung-RADS scores was associated with patient nonadherence over time.

   Among 1979 included patients, 1111 (56.1%) were aged 65 years or older at baseline screening (mean [SD] age, 65.3 [6.6] years), and 1176 (59.4%) were male. The odds of being nonadherent were lower among patients with a baseline Lung-RADS score of 1 or 2 vs 3 (adjusted odds ratio [AOR], 0.35; 95% CI, 0.25-0.50), 4A (AOR, 0.21; 95% CI, 0.13-0.33), or 4B/X, (AOR, 0.10; 95% CI, 0.05-0.19); with a postgraduate vs college degree (AOR, 0.70; 95% CI, 0.53-0.92); with a family history of lung cancer vs no family history (AOR, 0.74; 95% CI, 0.59-0.93); with a high age-adjusted Charlson Comorbidity Index score (≥4) vs a low score (0 or 1) (AOR, 0.67; 95% CI, 0.46-0.98); in the high vs low income category (AOR, 0.79; 95% CI, 0.65-0.98); and referred by physicians from pulmonary or thoracic-related departments vs another department (AOR, 0.56; 95% CI, 0.44-0.73). Among 830 eligible patients who had completed at least 2 screening examinations, the adjusted odds of being nonadherent to Lung-RADS recommendations at the following screening were increased in patients with consecutive Lung-RADS scores of 1 to 2 (AOR, 1.38; 95% CI, 1.12-1.69).

   In this retrospective cohort study, patients with consecutive negative lung cancer screening results were more likely to be nonadherent with follow-up recommendations. These individuals are potential candidates for tailored outreach to improve adherence to recommended annual lung cancer screening.

    

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