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1. Microbiological characteristics of bacteremias among COVID-19 hospitalized patients in a tertiary referral hospital in Northern Greece during the second epidemic wave

   https://doi.org/10.1093/femsmc/xtab021  

   Protonotariou, Efthymia ; Mantzana, Paraskevi ; Meletis, Georgios ; Tychala, Areti ; Kassomenaki, Angeliki ; Vasilaki, Olga ; Kagkalou, Georgia ; Gkeka, Ioanna ; Archonti, Maria ; Kati, Styliani ; et al ( December 2021 , FEMS Microbes)

   Northern Greece was struck by an intense second COVID-19 (coronavirus disease 2019) epidemic wave during the fall of 2020. Because of the coinciding silent epidemic of multidrug-resistant organisms, the handling of COVID-19 patients became even more challenging. In the present study, the microbiological characteristics of bacteremias in confirmed cases of hospitalized COVID-19 patients were determined. Data from 1165 patients hospitalized between September and December 2020 were reviewed regarding the frequency of bloodstream infections, the epidemiology and the antibiotic susceptibility profiles of the causative bacteria. The hospital's antibiotic susceptibility data for all major nosocomial pathogens isolated from bacteremias of COVID-19 patients between September and December 2020 versus those between September and December 2019 were also compared. Overall, 122 patients developed bacteremia (10.47%). The average of time interval between hospitalization date and development of bacteremia was 13.98 days. Admission to ICU occurred in 98 out of 122 patients with an average stay time of 15.85 days and 90.81% in-hospital mortality. In total, 166 pathogens were recovered including 114 Gram-negative bacteria and 52 Gram-positive cocci. Acinetobacter baumannii was the most frequent (n = 51) followed by Klebsiella pneumoniae (n = 45) and Enterococcus faecium (n = 31). Bacteremias in hospitalized COVID-19 patientsmore »were related with prolonged time of hospitalization and higher in-hospital mortality, and the isolated microorganisms represented the bacterial species that were present in our hospital before the COVID-19 pandemic. Worryingly, the antibiotic resistance rates were increased compared with the pre-pandemic era for all major opportunistic bacterial pathogens. The pandemic highlighted the need for continuous surveillance of patients with prolonged hospitalization.

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2. Patient-specific COVID-19 resource utilization prediction using fusion AI model

   https://doi.org/10.1038/s41746-021-00461-0  

   Tariq, Amara ; Celi, Leo Anthony ; Newsome, Janice M. ; Purkayastha, Saptarshi ; Bhatia, Neal Kumar ; Trivedi, Hari ; Gichoya, Judy Wawira ; Banerjee, Imon ( June 2021 , npj Digital Medicine)

   The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importancemore »analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.

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3. Optimal SARS-CoV-2 vaccine allocation using real-time attack-rate estimates in Rhode Island and Massachusetts

   https://doi.org/10.1186/s12916-021-02038-w  

   Tran, Thu Nguyen-Anh ; Wikle, Nathan B. ; Albert, Emmy ; Inam, Haider ; Strong, Emily ; Brinda, Karel ; Leighow, Scott M. ; Yang, Fuhan ; Hossain, Sajid ; Pritchard, Justin R. ; et al ( December 2021 , BMC Medicine)

   Abstract Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Resultsmore »We find that allocating a substantial proportion (>75 % ) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28 % ) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.« less
4. Effect of Race and Ethnicity on In-Hospital Mortality in Patients with COVID-2019

   https://doi.org/10.18865/ed.31.3.389  

   Qureshi, Adnan I. ; Baskett, William I. ; Huang, Wei ; Shyu, Daniel ; Myers, Danny ; Lobanova, Iryna ; Naqvi, S. Hasan ; Thompson, Vetta S. ; Shyu, Chi-Ren ( July 2021 , Ethnicity & Disease)

   Objective: To identify differences in short-term outcomes of patients with coronavirus disease 2019 (COVID-19) according to various racial/ethnic groups.Design: Analysis of Cerner de-identified COVID-19 dataset.Setting: A total of 62 health care facilities.Participants: The cohort included 49,277 adult COVID-19 patients who were hospitalized from December 1, 2019 to November 13, 2020.Methods: We compared patients’ age, gender, individual components of Charl­son and Elixhauser comorbidities, medical complications, use of do-not-resuscitate, use of palliative care, and socioeconomic status between various racial and/or ethnic groups. We further compared the rates of in-hos­pital mortality and non-routine discharges between various racial and/or ethnic groups.Main Outcome Measures: The primary outcome of interest was in-hospital mortali­ty. The secondary outcome was non-routine discharge (discharge to destinations other than home, such as short-term hospitals or other facilities including intermediate care and skilled nursing homes).Results: Compared with White patients, in-hospital mortality was significantly higher among African American (OR 1.5; 95%CI:1.3-1.6, P<.001), Hispanic (OR1.4; 95%CI:1.3-1.6, P<.001), and Asian or Pacific Islander (OR 1.5; 95%CI: 1.1-1.9, P=.002) patients after adjustment for age and gender, Elixhauser comorbidities, do-not-resuscitate status, palliative care use, and socioeconomic status.Conclusions: Our study found that, among hospitalized patients with COVID-2019, African American, Hispanic, and Asian or Pacific Islander patients had increasedmore »mortality compared with White patients after adjusting for sociodemographic factors, comorbidities, and do-not-resuscitate/pallia­tive care status. Our findings add additional perspective to other recent studies. Ethn Dis. 2021;31(3):389-398; doi:10.18865/ed.31.3.389« less
5. Excess mortality in Ukraine during the course of COVID-19 pandemic in 2020–2021

   https://doi.org/10.1038/s41598-023-33113-2  

   Shishkin, Aleksandr ; Lhewa, Pema ; Yang, Chen ; Gankin, Yuriy ; Chowell, Gerardo ; Norris, Michael ; Skums, Pavel ; Kirpich, Alexander ( April 2023 , Scientific Reports)

   In this work, the COVID-19 pandemic burden in Ukraine is investigated retrospectively using the excess mortality measures during 2020–2021. In particular, the epidemic impact on the Ukrainian population is studied via the standardized both all-cause and cause-specific mortality scores before and during the epidemic. The excess mortality counts during the pandemic were predicted based on historic data using parametric and nonparametric modeling and then compared with the actual reported counts to quantify the excess. The corresponding standardized mortalityP-score metrics were also compared with the neighboring countries. In summary, there were three “waves” of excess all-cause mortality in Ukraine in December 2020, April 2021 and November 2021 with excess of 32%, 43% and 83% above the expected mortality. Each new “wave” of the all-cause mortality was higher than the previous one and the mortality “peaks” corresponded in time to three “waves” of lab-confirmed COVID-19 mortality. The lab-confirmed COVID-19 mortality constituted 9% to 24% of the all-cause mortality during those three peak months. Overall, the mortality trends in Ukraine over time were similar to neighboring countries where vaccination coverage was similar to that in Ukraine. For cause-specific mortality, the excess observed was due to pneumonia as well as circulatory system diseasemore »categories that peaked at the same times as the all-cause and lab-confirmed COVID-19 mortality, which was expected. The pneumonias as well as circulatory system disease categories constituted the majority of all cases during those peak times. The seasonality in mortality due to the infectious and parasitic disease category became less pronounced during the pandemic. While the reported numbers were always relatively low, alcohol-related mortality also declined during the pandemic.

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