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1. 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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2. Health diagnosis associated with COVID-19 death in the United States: A retrospective cohort study using electronic health records

   Joseph, Mariam; Li, Qiwei; Shin, Sunyoung (March 2025, PloS one)

   Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes. 

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3. Prescribed fires, smoke exposure, and hospital utilization among heart failure patients

   https://doi.org/10.1186/s12940-023-01032-4  

   Raab, Henry; Moyer, Joshua; Afrin, Sadia; Garcia-Menendez, Fernando; Ward-Caviness, Cavin K (December 2023, Environmental Health)

   Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients’ hospital utilization. Methods: We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients’ primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI). Results: Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies. Conclusions: Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires. 

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4. 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)

   BackgroundAlthough 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. MethodsAdults 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. Results2,187 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. ConclusionPostoperative 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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5. Initiation of Continuous Glucose Monitoring Is Linked to Improved Glycemic Control and Fewer Clinical Events in Type 1 and Type 2 Diabetes in the Veterans Health Administration

   https://doi.org/10.2337/dc22-2189  

   Reaven, Peter D.; Newell, Michelle; Rivas, Salvador; Zhou, Xinkai; Norman, Gregory J.; Zhou, Jin J. (February 2023, Diabetes Care)

   OBJECTIVETo determine the benefit of starting continuous glucose monitoring (CGM) in adult-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) with regard to longer-term glucose control and serious clinical events. RESEARCH DESIGN AND METHODSA retrospective observational cohort study within the Veterans Affairs Health Care System was used to compare glucose control and hypoglycemia- or hyperglycemia-related admission to an emergency room or hospital and all-cause hospitalization between propensity score overlap weighted initiators of CGM and nonusers over 12 months. RESULTSCGM users receiving insulin (n = 5,015 with T1D and n = 15,706 with T2D) and similar numbers of nonusers were identified from 1 January 2015 to 31 December 2020. Declines in HbA1c were significantly greater in CGM users with T1D (−0.26%; 95% CI −0.33, −0.19%) and T2D (−0.35%; 95% CI −0.40, −0.31%) than in nonusers at 12 months. Percentages of patients achieving HbA1c <8 and <9% after 12 months were greater in CGM users. In T1D, CGM initiation was associated with significantly reduced risk of hypoglycemia (hazard ratio [HR] 0.69; 95% CI 0.48, 0.98) and all-cause hospitalization (HR 0.75; 95% CI 0.63, 0.90). In patients with T2D, there was a reduction in risk of hyperglycemia in CGM users (HR 0.87; 95% CI 0.77, 0.99) and all-cause hospitalization (HR 0.89; 95% CI 0.83, 0.97). Several subgroups (based on baseline age, HbA1c, hypoglycemic risk, or follow-up CGM use) had even greater responses. CONCLUSIONSIn a large national cohort, initiation of CGM was associated with sustained improvement in HbA1c in patients with later-onset T1D and patients with T2D using insulin. This was accompanied by a clear pattern of reduced risk of admission to an emergency room or hospital for hypoglycemia or hyperglycemia and of all-cause hospitalization. 

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