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1. Mortality in infantile spasms: A hospital‐based study

   https://doi.org/10.1111/epi.16468  

   Harini, Chellamani ; Nagarajan, Elanagan ; Bergin, Ann M. ; Pearl, Phillip ; Loddenkemper, Tobias ; Takeoka, Masanori ; Morrison, Peter F. ; Coulter, David ; Harappanahally, Gita ; Marti, Candice ; et al ( March 2020 , Epilepsia)

   To determine risk factors and causes for mortality during childhood in patients with infantile spasms (IS). We describe the overall goals of care for those who died.

   This is a retrospective chart review of IS patients born between 2000 and 2011. We examined potential risk factors for mortality, including etiology, neurologic impairment, medication use, persistence of epileptic spasms, and comorbid systemic involvement (requirement for G‐tube feedings, respiratory interventions). For patients who died, we describe cause of death and resuscitation status or end‐of‐life care measures.

   We identified 150 IS patients with median follow‐up of 12 years. During the study period, 25 (17%) patients died, 13 before 5 years of age. Univariate analysis demonstrated that developmental delay, identifiable etiology, hormonal use for IS, persistence of epileptic spasms, polypharmacy with antiseizure medications, refractory epilepsy, respiratory system comorbidity, and the need for a G‐tube were significant risk factors for mortality. In a multivariate analysis, mortality was predicted by persistence of epileptic spasms (odds ratio [OR] = 4.30, 95% confidence interval [CI] = 1.11‐16.67,P = .035) and significant respiratory system comorbidity (OR = 12.75, 95% CI = 2.88‐56.32,P = .001). Mortality was epilepsy‐related in one‐third of patients who died with sudden unexpected death in epilepsy (SUDEP), accounting for 88% of epilepsy‐related deaths. Most deaths before age 5 years were related to respiratory failure, and SUDEP was less common (17%) whereas SUDEP was more common (45%) with deaths after 5 years. For the majority (67%) of patients with early mortality, an end‐of‐life care plan was in place (based on documentation of resuscitation status, comfort measures, or decision not to escalate medical care).

   Mortality at our single‐center IS cohort was 17%, and persistence of epileptic spasms and comorbid respiratory system disorders were the most important determinants of mortality. Early deaths were related to neurological impairments/comorbidities. SUDEP was more common in children who died after 5 years of age than in those who died younger than 5 years.

    

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2. Vascular comorbidities worsen prognosis of patients with heart failure hospitalised with COVID-19

   https://doi.org/10.1136/openhrt-2021-001668  

   Mok, Jacob ; Malpartida, Juan Carlos ; O'Dell, Kimberly ; Davis, Joshua ; Gao, Cuilan ; Manyam, Harish ( June 2021 , Open Heart)

   null (Ed.)

   Background Prior diagnosis of heart failure (HF) is associated with increased length of hospital stay (LOS) and mortality from COVID-19. Associations between substance use, venous thromboembolism (VTE) or peripheral arterial disease (PAD) and its effects on LOS or mortality in patients with HF hospitalised with COVID-19 remain unknown. Objective This study identified risk factors associated with poor in-hospital outcomes among patients with HF hospitalised with COVID-19. Methods Case–control study was conducted of patients with prior diagnosis of HF hospitalised with COVID-19 at an academic tertiary care centre from 1 January 2020 to 28 February 2021. Patients with HF hospitalised with COVID-19 with risk factors were compared with those without risk factors for clinical characteristics, LOS and mortality. Multivariate regression was conducted to identify multiple predictors of increased LOS and in-hospital mortality in patients with HF hospitalised with COVID-19. Results Total of 211 patients with HF were hospitalised with COVID-19. Women had longer LOS than men (9 days vs 7 days; p<0.001). Compared with patients without PAD or ischaemic stroke, patients with PAD or ischaemic stroke had longer LOS (7 days vs 9 days; p=0.012 and 7 days vs 11 days, p<0.001, respectively). Older patients (aged 65 and above) had increased in-hospital mortality compared with younger patients (adjusted OR: 1.04; 95% CI 1.00 to 1.07; p=0.036). Prior diagnosis of VTE increased mortality more than threefold in patients with HF hospitalised with COVID-19 (adjusted OR: 3.33; 95% CI 1.29 to 8.43; p=0.011). Conclusion Vascular diseases increase LOS and mortality in patients with HF hospitalised with COVID-19. 

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3. Comparative epidemiology of five waves of COVID-19 in Mexico, March 2020–August 2022

   https://doi.org/10.1186/s12879-022-07800-w  

   Ascencio-Montiel, Iván de Jesús ; Ovalle-Luna, Oscar David ; Rascón-Pacheco, Ramón Alberto ; Borja-Aburto, Victor Hugo ; Chowell, Gerardo ( October 2022 , BMC Infectious Diseases)

   The Mexican Institute of Social Security (IMSS) is the largest health care provider in Mexico, covering about 48% of the Mexican population. In this report, we describe the epidemiological patterns related to confirmed cases, hospitalizations, intubations, and in-hospital mortality due to COVID-19 and associated factors, during five epidemic waves recorded in the IMSS surveillance system.

   We analyzed COVID-19 laboratory-confirmed cases from the Online Epidemiological Surveillance System (SINOLAVE) from March 29th, 2020, to August 27th, 2022. We constructed weekly epidemic curves describing temporal patterns of confirmed cases and hospitalizations by age, gender, and wave. We also estimated hospitalization, intubation, and hospital case fatality rates. The mean days of in-hospital stay and hospital admission delay were calculated across five pandemic waves. Logistic regression models were employed to assess the association between demographic factors, comorbidities, wave, and vaccination and the risk of severe disease and in-hospital death.

   A total of 3,396,375 laboratory-confirmed COVID-19 cases were recorded across the five waves. The introduction of rapid antigen testing at the end of 2020 increased detection and modified epidemiological estimates. Overall, 11% (95% CI 10.9, 11.1) of confirmed cases were hospitalized, 20.6% (95% CI 20.5, 20.7) of the hospitalized cases were intubated, and the hospital case fatality rate was 45.1% (95% CI 44.9, 45.3). The mean in-hospital stay was 9.11 days, and patients were admitted on average 5.07 days after symptoms onset. The most recent waves dominated by the Omicron variant had the highest incidence. Hospitalization, intubation, and mean hospitalization days decreased during subsequent waves. The in-hospital case fatality rate fluctuated across waves, reaching its highest value during the second wave in winter 2020. A notable decrease in hospitalization was observed primarily among individuals ≥ 60 years. The risk of severe disease and death was positively associated with comorbidities, age, and male gender; and declined with later waves and vaccination status.

   During the five pandemic waves, we observed an increase in the number of cases and a reduction in severity metrics. During the first three waves, the high in-hospital fatality rate was associated with hospitalization practices for critical patients with comorbidities.

    

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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. Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches

   https://doi.org/10.3389/fmed.2023.1165912  

   Kobayashi, Yuta ; Peng, Yu-Chung ; Yu, Evan ; Bush, Brian ; Jung, Youn-Hoa ; Murphy, Zachary ; Goeddel, Lee ; Whitman, Glenn ; Venkataraman, Archana ; Brown, Charles H. ( September 2023 , Frontiers in Medicine)

   Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance.

   Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data.

   2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features.

   Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.

    

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