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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. Investigating distress levels in patients with metastatic spine disease undergoing surgical intervention

   https://doi.org/10.3171/2025.2.FOCUS24963  

   Rowe, Dana G; Woo, Joshua; O’Callaghan, Ellen; Barrett, Connor; Luo, Emily J; Zachem, Tanner J; Dalton, Tara; Goodin, Michael; Crowell, Kerri-Anne; Johnson, Margaret O; et al (May 2025, Neurosurgical Focus)

   OBJECTIVECancer patients often experience high levels of distress, which are particularly pronounced in the perioperative period. However, there is a dearth of research on distress rates in patients with metastatic spine disease (MSD). This study aims to assess pre- and postoperative distress levels in patients with MSD undergoing surgical intervention, as well as the association between distress and sociodemographic factors. METHODSThe authors retrospectively queried electronic medical records from a single institution for demographic and clinical data on patients with MSD who underwent surgical intervention between 2015 and 2023. Data included pre- (within 30 days of surgery) and postoperative (within 30 and 90 days of surgery) National Comprehensive Cancer Network’s distress thermometer (NCCN-DT) scores. The proportion of patients with clinically significant distress (DT score ≥ 4) at each time point was examined, as well as changes between baseline distress and distress 30 days postoperatively. The association between clinically significant distress and sex, age, race/ethnicity, and marital status was assessed. A p value < 0.05 was considered significant. RESULTSThe study identified 265 patients with complete NCCN-DT questionnaires. Nearly half (47.5%) of the patients were female, with 66.0% identifying as Caucasian/White. The mean (± standard deviation) age at surgery was 61.4 ± 12.1 years. Preoperatively, the mean distress score was 3.6 ± 3.1 (range 0–10), with 89 (46.4%) of 192 patients reporting moderate to severe distress (DT ≥ 4). The mean distress score at 30 days postoperatively was 3.2 ± 3.0 (range 0–10), with 43.8% of patients reporting moderate to severe distress. At 90 days postoperatively, the mean distress score was 2.3 ± 2.5 (range 0–9) with 26.6% of patients reporting moderate to severe levels. Non-White patients had significantly higher preoperative distress than their White counterparts (p = 0.03). CONCLUSIONSDistress is a common experience among patients with MSD undergoing surgical intervention. Preoperatively, nearly half of these patients report moderate to severe distress, with distress levels remaining elevated through the 1st month after surgery. These findings highlight the critical need for timely psychosocial interventions to address distress at key stages of the surgical process. Race-based differences in distress rates emphasize the importance of developing targeted support strategies for more vulnerable groups. 

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3. Regional Vessel Density Reduction in the Macula and Optic Nerve Head of Patients With Pre-Perimetric Primary Open Angle Glaucoma

   https://doi.org/10.1097/IJG.0000000000002310  

   Verticchio_Vercellin, Alice; Siesky, Brent; Antman, Gal; Oddone, Francesco; Chang, Michael; Eckert, George; Arciero, Julia; Kellner, Rebecca L; Fry, Brendan; Coleman-Belin, Janet; et al (January 2023, Journal of Glaucoma)

   Précis:Capillary and neuronal tissue loss occur both globally and with regional specificity in pre-perimetric glaucoma patients at the level of the optic nerve and macula, with perifovea regions affected earlier than parafovea areas. Purpose:To investigate optic nerve head (ONH) and macular vessel densities (VD) and structural parameters assessed by optical coherence tomography angiography in pre-perimetric open angle glaucoma (ppOAG) patients and healthy controls. Materials and Methods:In all, 113 healthy and 79 ppOAG patients underwent global and regional (hemispheric/quadrants) assessments of retinal, ONH, and macular vascularity and structure, including ONH parameters, retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness. Comparisons between outcomes in ppOAG and controls were adjusted for age, sex, race, BMI, diabetes, and hypertension, withP<0.05 considered statistically significant. Results:In ppOAG compared with healthy controls: RNFL thicknesses were statistically significantly lower for all hemispheres, quadrants, and sectors (P<0.001–0.041); whole image peripapillary all and small blood vessels VD were statistically significantly lower for all the quadrants (P<0.001–0.002), except for the peripapillary small vessels in the temporal quadrant (ppOAG: 49.66 (8.40), healthy: 53.45 (4.04);P=0.843); GCC and inner and full macular thicknesses in the parafoveal and perifoveal regions were significantly lower in all the quadrants (P=0.000–P=0.033); several macular VD were significantly lower (P=0.006–0.034), with the exceptions of macular center, parafoveal superior and inferior quadrant, and perifoveal superior quadrant (P>0.05). Conclusions:In ppOAG patients, VD biomarkers in both the macula and ONH, alongside RNFL, GCC, and macular thickness, were significantly reduced before detectable visual field loss with regional specificity. The most significant VD reduction detected was in the peripheric (perifovea) regions. Macular and ONH decrease in VD may serve as early biomarkers of glaucomatous disease. 

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4. Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis

   https://doi.org/10.1093/jamia/ocaf036  

   Kamran, Fahad; Tjandra, Donna; Valley, Thomas_S; Prescott, Hallie_C; Shah, Nigam_H; Liu, Vincent_X; Horvitz, Eric; Wiens, Jenna (March 2025, Journal of the American Medical Informatics Association)

   Abstract ObjectivesTo quantify differences between (1) stratifying patients by predicted disease onset risk alone and (2) stratifying by predicted disease onset risk and severity of downstream outcomes. We perform a case study of predicting sepsis. Materials and MethodsWe performed a retrospective analysis using observational data from Michigan Medicine at the University of Michigan (U-M) between 2016 and 2020 and the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2012. We measured the correlation between the estimated sepsis risk and the estimated effect of sepsis on mortality using Spearman’s correlation. We compared patients stratified by sepsis risk with patients stratified by sepsis risk and effect of sepsis on mortality. ResultsThe U-M and BIDMC cohorts included 7282 and 5942 ICU visits; 7.9% and 8.1% developed sepsis, respectively. Among visits with sepsis, 21.9% and 26.3% experienced mortality at U-M and BIDMC. The effect of sepsis on mortality was weakly correlated with sepsis risk (U-M: 0.35 [95% CI: 0.33-0.37], BIDMC: 0.31 [95% CI: 0.28-0.34]). High-risk patients identified by both stratification approaches overlapped by 66.8% and 52.8% at U-M and BIDMC, respectively. Accounting for risk of mortality identified an older population (U-M: age = 66.0 [interquartile range—IQR: 55.0-74.0] vs age = 63.0 [IQR: 51.0-72.0], BIDMC: age = 74.0 [IQR: 61.0-83.0] vs age = 68.0 [IQR: 59.0-78.0]). DiscussionPredictive models that guide selective interventions ignore the effect of disease on downstream outcomes. Reformulating patient stratification to account for the estimated effect of disease on downstream outcomes identifies a different population compared to stratification on disease risk alone. ConclusionModels that predict the risk of disease and ignore the effects of disease on downstream outcomes could be suboptimal for stratification. 

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5. 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)

   BACKGROUND: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). METHODS: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. RESULTS: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). CONCLUSIONS: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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