Fungal Infections in the ICU during the COVID-19 Pandemic in Mexico.

Background: Invasive Fungal Infections (IFI) are emergent complications of COVID-19. In this study, we aim to describe the prevalence, related factors, and outcomes of IFI in critical COVID-19 patients. Methods: We conducted a nested case-control study of all COVID-19 patients in the intensive care unit (ICU) who developed any IFI and matched age and sex controls for comparison (1:1) to evaluate IFI-related factors. Descriptive and comparative analyses were made, and the risk factors for IFI were compared versus controls. Results: We found an overall IFI prevalence of 9.3% in COVID-19 patients in the ICU, 5.6% in COVID-19-associated pulmonary aspergillosis (CAPA), and 2.5% in invasive candidiasis (IC). IFI patients had higher SOFA scores, increased frequency of vasopressor use, myocardial injury, and more empirical antibiotic use. CAPA was classified as possible in 68% and 32% as probable by ECMM/ISHAM consensus criteria, and 57.5% of mortality was found. Candidemia was more frequent for C. parapsilosis Fluconazole resistant outbreak early in the pandemic, with a mortality of 28%. Factors related to IFI in multivariable analysis were SOFA score > 2 (aOR 5.1, 95% CI 1.5-16.8, p = 0.007) and empiric antibiotics for COVID-19 (aOR 30, 95% CI 10.2-87.6, p = <0.01). Conclusions: We found a 9.3% prevalence of IFIs in critically ill patients with COVID-19 in a single center in Mexico; factors related to IFI were associated with higher SOFA scores and empiric antibiotic use for COVID-19. CAPA is the most frequent type of IFI. We did not find a mortality difference.

Correction: In-hospital mortality from severe COVID-19 in a tertiary care center in Mexico City; causes of death, risk factors and the impact of hospital saturation.

[This corrects the article DOI: 10.1371/journal.pone.0245772.].

NAFLD determined by Dallas Steatosis Index is associated with poor outcomes in COVID-19 pneumonia: a cohort study.

Coronavirus disease 2019 is a worldwide health challenge. Liver steatosis diagnosis based on imaging studies has been implicated in poor outcomes of COVID-19 pneumonia, but results are inconsistent. The Dallas Steatosis Index (DSI) is an available calculator developed to identify patients with non-alcoholic fatty liver disease (NAFLD). We hypothesized that it would be associated with in-hospital mortality, intensive care unit admission (ICU), and invasive mechanical ventilation (IMV). We conducted a retrospective cohort study on inpatients with confirmed COVID-19 pneumonia between February 26 and April 11, 2020. We computed the DSI on admission, and patients with high DSI were considered with NAFLD. We employed logistic regression to study the association between NAFLD, mortality, ICU admission, and IMV. We studied the association between liver steatosis on computed tomography (CT) and these outcomes, and also between Metabolic Associated Fatty Liver Disease (MAFLD) based on CT findings and risk factors and the outcomes. 470 patients were included; 359 had NAFLD according to the DSI. They had a higher frequency of type 2 diabetes (31% vs 14%, p < 0.001), obesity (58% vs 14%, p < 0.001), and arterial hypertension (34% vs 22%, p = 0.02). In univariable analysis, NAFLD was associated with mortality, ICU admission, and IMV. Liver steatosis by CT and MAFLD were not associated with any of these outcomes. In multivariable logistic regression, high DSI remained significantly associated with IMV and death. High DSI, which can be easily computed on admission, was associated with IMV and death, and its use to better stratify the prognosis of these patients should be explored. On the other hand, liver steatosis by CT and MAFLD were not associated with poor outcomes.

Adverse Effects Associated With the Use of Antimalarials During The COVID-19 Pandemic in a Tertiary Care Center in Mexico City.

Background: Antimalarial drugs were widely used as experimental therapies against COVID-19 in the initial stages of the pandemic. Despite multiple randomized controlled trials demonstrating unfavorable outcomes in both efficacy and adverse effects, antimalarial drugs are still prescribed in developing countries, especially in those experiencing recurrent COVID-19 crises (India and Brazil). Therefore, real-life experience and pharmacovigilance studies describing the use and side effects of antimalarials for COVID-19 in developing countries are still relevant. Objective: To describe the adverse effects associated with the use of antimalarial drugs in hospitalized patients with COVID-19 pneumonia at a reference center in Mexico City. Methods: We integrated a retrospective cohort with all adult patients hospitalized for COVID-19 pneumonia from March 13th, 2020, to May 17th, 2020. We compared the baseline characteristics (demographic and clinical) and the adverse effects between the groups of patients treated with and without antimalarial drugs. The mortality analysis was performed in 491 patients who received optimal care and were not transferred to other institutions (210 from the antimalarial group and 281 from the other group). Results: We included 626 patients from whom 38% (n = 235) received an antimalarial drug. The mean age was 51.2 ± 13.6 years, and 64% were males. At baseline, compared with the group treated with antimalarials, the group that did not receive antimalarials had more dyspnea (82 vs. 73%, p = 0.017) and cyanosis (5.3 vs. 0.9%, p = 0.009), higher respiratory rate (median of 28 vs. 24 bpm, p < 0.001), and lower oxygen saturation (median of 83 vs. 87%, p < 0.001). In the group treated with antimalarials, 120 patients had two EKG evaluations, from whom 12% (n = 16) prolonged their QTc from baseline in more than 50 ms, and six developed a ventricular arrhythmia. Regarding the trajectories of the liver function tests over time, no significant differences were found for the change in the mean value per day between the two groups. Among patients who received optimal care, the mortality was 16% (33/210) in those treated with antimalarials and 15% (41/281) in those not receiving antimalarials (RR 1.08, 95% 0.75-1.64, and adjusted RR 1.12, 95% CI 0.69-1.82). Conclusion: The adverse events in patients with COVID-19 treated with antimalarials were similar to those who did not receive antimalarials at institutions with rigorous pharmacological surveillance. However, they do not improve survival in patients who receive optimal medical care.

Low Thoracic Skeletal Muscle Area Is Not Associated With Negative Outcomes in Patients With COVID-19.

OBJECTIVE: Sarcopenia has been related to negative outcomes in different clinical scenarios from critical illness to chronic conditions. The aim of this study was to verify whether there was an association between low skeletal muscle index and in-hospital mortality, intensive care unit admission, and invasive mechanical ventilation need in hospitalized patients with COVID-19. DESIGN: This was a retrospective cohort study of a referral center for COVID-19. We included all consecutive patients admitted to the hospital between February 26 and May 15, 2020, with a confirmed diagnosis of COVID-19. Skeletal muscle index was assessed from a transverse computed tomography image at the level of twelfth thoracic vertebra with National Institutes of Health ImageJ software, and statistical analysis was performed to find an association between skeletal muscle index and in-hospital mortality, need of invasive mechanical ventilation, and intensive care unit admission. RESULTS: We included 519 patients, the median age was 51 (42-61) yrs, and 115 patients (22%) had low skeletal muscle index. On multivariable analysis, skeletal muscle index was not associated with mortality, intensive care unit admission, or invasive mechanical ventilation need nor in a subanalysis of patients 65 yrs or older. CONCLUSIONS: Skeletal muscle index determined by computed tomography at the level of twelfth thoracic vertebra was not associated with negative outcomes in hospitalized patients with COVID-19.

Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection.

BACKGROUND: Chronological age (CA) is a predictor of adverse coronavirus disease 2019 (COVID-19) outcomes; however, CA alone does not capture individual responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAgeAccel to predict adverse COVID-19 outcomes. Furthermore, we sought to model adaptive metabolic and inflammatory responses to severe SARS-CoV-2 infection using individual PhenoAge components. METHOD: In this retrospective cohort study, we assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAgeAccel were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (intensive care unit admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge components. RESULTS: We included 1068 subjects of whom 222 presented critical illness and 218 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated to PhenoAgeAccel >0 had higher risk of death and critical illness compared to those with lower values (log-rank p < .001). Using unsupervised clustering, we identified 4 adaptive responses to SARS-CoV-2 infection: (i) inflammaging associated with CA, (ii) metabolic dysfunction associated with cardiometabolic comorbidities, (iii) unfavorable hematological response, and (iv) response associated with favorable outcomes. CONCLUSIONS: Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

Impact of undiagnosed type 2 diabetes and pre-diabetes on severity and mortality for SARS-CoV-2 infection.

INTRODUCTION: Diabetes and hyperglycemia are risk factors for critical COVID-19 outcomes; however, the impact of pre-diabetes and previously unidentified cases of diabetes remains undefined. Here, we profiled hospitalized patients with undiagnosed type 2 diabetes and pre-diabetes to evaluate its impact on adverse COVID-19 outcomes. We also explored the role of de novo and intrahospital hyperglycemia in mediating critical COVID-19 outcomes. RESEARCH DESIGN AND METHODS: Prospective cohort of 317 hospitalized COVID-19 cases from a Mexico City reference center. Type 2 diabetes was defined as previous diagnosis or treatment with diabetes medication, undiagnosed diabetes and pre-diabetes using glycosylated hemoglobin (HbA1c) American Diabetes Association (ADA) criteria and de novo or intrahospital hyperglycemia as fasting plasma glucose (FPG) ≥140 mg/dL. Logistic and Cox proportional regression models were used to model risk for COVID-19 outcomes. RESULTS: Overall, 159 cases (50.2%) had type 2 diabetes and 125 had pre-diabetes (39.4%), while 31.4% of patients with type 2 diabetes were previously undiagnosed. Among 20.0% of pre-diabetes cases and 6.1% of normal-range HbA1c had de novo hyperglycemia. FPG was the better predictor for critical COVID-19 compared with HbA1c. Undiagnosed type 2 diabetes (OR: 5.76, 95% CI 1.46 to 27.11) and pre-diabetes (OR: 4.15, 95% CI 1.29 to 16.75) conferred increased risk of severe COVID-19. De novo/intrahospital hyperglycemia predicted critical COVID-19 outcomes independent of diabetes status. CONCLUSIONS: Undiagnosed type 2 diabetes, pre-diabetes and de novo hyperglycemia are risk factors for critical COVID-19. HbA1c must be measured early to adequately assess individual risk considering the large rates of undiagnosed type 2 diabetes in Mexico.

Substantial reduction of healthcare facility-onset Clostridioides difficile infection (HO-CDI) rates after conversion of a hospital for exclusive treatment of COVID-19 patients.

Healthcare facility-onset Clostridioides difficile infection rates substantially dropped in a Mexican hospital after its conversion to a full COVID-19 setting, despite heavy contamination of the environment the previous year. Better adherence to hand hygiene and contact precautions may help explain this finding.

Validation and repurposing of the MSL-COVID-19 score for prediction of severe COVID-19 using simple clinical predictors in a triage setting: The Nutri-CoV score.

BACKGROUND: During the COVID-19 pandemic, risk stratification has been used to decide patient eligibility for inpatient, critical and domiciliary care. Here, we sought to validate the MSL-COVID-19 score, originally developed to predict COVID-19 mortality in Mexicans. Also, an adaptation of the formula is proposed for the prediction of COVID-19 severity in a triage setting (Nutri-CoV). METHODS: We included patients evaluated from March 16th to August 17th, 2020 at the Instituto Nacional de Ciencias Médicas y Nutrición, defining severe COVID-19 as a composite of death, ICU admission or requirement for intubation (n = 3,007). We validated MSL-COVID-19 for prediction of mortality and severe disease. Using Elastic Net Cox regression, we trained (n = 1,831) and validated (n = 1,176) a model for prediction of severe COVID-19 using MSL-COVID-19 along with clinical assessments obtained at a triage setting. RESULTS: The variables included in MSL-COVID-19 are: pneumonia, early onset type 2 diabetes, age > 65 years, chronic kidney disease, any form of immunosuppression, COPD, obesity, diabetes, and age <40 years. MSL-COVID-19 had good performance to predict COVID-19 mortality (c-statistic = 0.722, 95%CI 0.690-0.753) and severity (c-statistic = 0.777, 95%CI 0.753-0.801). The Nutri-CoV score includes the MSL-COVID-19 plus respiratory rate, and pulse oximetry. This tool had better performance in both training (c-statistic = 0.797, 95%CI 0.765-0.826) and validation cohorts (c-statistic = 0.772, 95%CI 0.0.745-0.800) compared to other severity scores. CONCLUSIONS: MSL-COVID-19 predicts inpatient COVID-19 lethality. The Nutri-CoV score is an adaptation of MSL-COVID-19 to be used in a triage environment. Both scores have been deployed as web-based tools for clinical use in a triage setting.