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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. Evaluation of malnutrition as a predictor of adverse outcomes in febrile neutropenia associated with paediatric haematological malignancies

   https://doi.org/10.1111/jpc.13233  

   Chaudhuri, Jasodhara ; Biswas, Tamoghna ; Datta, Jyotishka ; Sabui, Tapas Kumar ; Chatterjee, Sukanta ; Ray, Somosri ; Raychaudhuri, Dibyendu ; Mandal, Kalyanbrata ; Chatterjee, Kaushani ; Chakraborty, Swapna ( July 2016 , Journal of Paediatrics and Child Health)

   Malnutrition has been reported in the literature to be adversely associated with outcomes in paediatric malignancies. Our objective in this paper was to evaluate malnutrition as a potential predictor for adverse outcomes in febrile neutropenia associated with haematological malignancies.

   A prospective observational study was performed in a tertiary care teaching hospital of Kolkata, India. Forty‐eight participants, suffering from haematological malignancy, were included. Participants were included if they experienced at least one episode of febrile neutropenia. For children aged <5 years, weight for height, height for age and weight for age were used as criteria for defining malnutrition, while body mass index for age was used in children ≥5 years. A total of 162 episodes of febrile neutropenia were studied.

   Thirty patients (30/48, 62.5%) included in the study had malnutrition. In bivariate analyses at patient level, there is a strong association between malnutrition and death (odds ratio (OR) 7.286, 95% confidence interval (CI) 0.838–63.345, one‐tailedP = 0.044), and life‐threatening complications show a moderate trend towards significance (OR 3.333, 95% CI 0.791–14.052, one‐tailedP = 0.084). Survival functions were significantly different between malnourished and non‐malnourished children (log rank test χ² = 4.609, degree of freedom = 1,P = 0.032). Wasting was associated with life‐threatening complications in children aged <5 years (OR 14, 95% CI 1.135–172.642, one‐tailedP = 0.036). Logistic regression analyses at episode level revealed that phase of treatment and respiratory system involvement were significant predictors of death, while malnutrition was not.

   Malnutrition may be a potential predictor of mortality in febrile neutropenia.

    

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3. Estimated US Pediatric Hospitalizations and School Absenteeism Associated With Accelerated COVID-19 Bivalent Booster Vaccination

   https://doi.org/10.1001/jamanetworkopen.2023.13586  

   Fitzpatrick, Meagan C. ; Moghadas, Seyed M. ; Vilches, Thomas N. ; Shah, Arnav ; Pandey, Abhishek ; Galvani, Alison P. ( May 2023 , JAMA Network Open)

   Importance Adverse outcomes of COVID-19 in the pediatric population include disease and hospitalization, leading to school absenteeism. Booster vaccination for eligible individuals across all ages may promote health and school attendance. Objective To assess whether accelerating COVID-19 bivalent booster vaccination uptake across the general population would be associated with reduced pediatric hospitalizations and school absenteeism. Design, Setting, and Participants In this decision analytical model, a simulation model of COVID-19 transmission was fitted to reported incidence data from October 1, 2020, to September 30, 2022, with outcomes simulated from October 1, 2022, to March 31, 2023. The transmission model included the entire age-stratified US population, and the outcome model included children younger than 18 years. Interventions Simulated scenarios of accelerated bivalent COVID-19 booster campaigns to achieve uptake that was either one-half of or similar to the age-specific uptake observed for 2020 to 2021 seasonal influenza vaccination in the eligible population across all age groups. Main Outcomes and Measures The main outcomes were estimated hospitalizations, intensive care unit admissions, and isolation days of symptomatic infection averted among children aged 0 to 17 years and estimated days of school absenteeism averted among children aged 5 to 17 years under the accelerated bivalent booster campaign simulated scenarios. Results Among children aged 5 to 17 years, a COVID-19 bivalent booster campaign achieving age-specific coverage similar to influenza vaccination could have averted an estimated 5 448 694 (95% credible interval [CrI], 4 936 933-5 957 507) days of school absenteeism due to COVID-19 illness. In addition, the booster campaign could have prevented an estimated 10 019 (95% CrI, 8756-11 278) hospitalizations among the pediatric population aged 0 to 17 years, of which 2645 (95% CrI, 2152-3147) were estimated to require intensive care. A less ambitious booster campaign with only 50% of the age-specific uptake of influenza vaccination among eligible individuals could have averted an estimated 2 875 926 (95% CrI, 2 524 351-3 332 783) days of school absenteeism among children aged 5 to 17 years and an estimated 5791 (95% CrI, 4391-6932) hospitalizations among children aged 0 to 17 years, of which 1397 (95% CrI, 846-1948) were estimated to require intensive care. Conclusions and Relevance In this decision analytical model, increased uptake of bivalent booster vaccination among eligible age groups was associated with decreased hospitalizations and school absenteeism in the pediatric population. These findings suggest that although COVID-19 prevention strategies often focus on older populations, the benefits of booster campaigns for children may be substantial. 

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4. Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population

   https://doi.org/10.1093/jamia/ocac062  

   Hao, Boran ; Hu, Yang ; Sotudian, Shahabeddin ; Zad, Zahra ; Adams, William G. ; Assoumou, Sabrina A. ; Hsu, Heather ; Mishuris, Rebecca G. ; Paschalidis, Ioannis C. ( May 2022 , Journal of the American Medical Informatics Association)

   To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.

   Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models.

   Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively.

   The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories.

   This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.

    

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5. Maternal epigenetic clocks measured during pregnancy do not predict gestational age at delivery or offspring birth outcomes: a replication study in metropolitan Cebu, Philippines

   https://doi.org/10.1186/s13148-022-01296-6  

   Ryan, Calen P. ; Rege, Raviraj J. ; Lee, Nanette R. ; Carba, Delia B. ; Kobor, Michael S. ; MacIsaac, Julie L. ; Lin, David S. ; Atashzay, Parmida ; Kuzawa, Christopher W. ( June 2022 , Clinical Epigenetics)

   Adverse birth outcomes, such as early gestational age and low birth weight, can have lasting effects on morbidity and mortality, with impacts that persist into adulthood. Identifying the maternal factors that contribute to adverse birth outcomes in the next generation is thus a priority. Epigenetic clocks, which have emerged as powerful tools for quantifying biological aging and various dimensions of physiological dysregulation, hold promise for clarifying relationships between maternal biology and infant health, including the maternal factors or states that predict birth outcomes. Nevertheless, studies exploring the relationship between maternal epigenetic age and birth outcomes remain few. Here, we attempt to replicate a series of analyses previously reported in a US-based sample, using a larger similarly aged sample (n = 296) of participants of a long-running study in the Philippines. New pregnancies were identified prospectively, dried blood spot samples were collected during the third trimester, and information was obtained on gestational age at delivery and offspring weight after birth. Genome-wide DNA methylation was assessed with the Infinium EPIC array. Using a suite of 15 epigenetic clocks, we only found one significant relationship: advanced age on the epigenetic clock trained on leptin predicted a significantly earlier gestational age at delivery (β = − 0.15,p = 0.009). Of the other 29 relationships tested predicting gestational age and offspring birth weight, none were statistically significant. In this sample of Filipino women, epigenetic clocks capturing multiple dimensions of biology and health do not predict birth outcomes in offspring.

    

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