Sex bias in basic and preclinical age-related hearing loss research

Abstract Objectives This study aims to determine if there is sex bias in basic and preclinical research on age-related hearing loss for the 10-year period of 2006–2015, prior to the NIH mandate of including sex as a biological variable in 2016. Design Manuscripts were identified in PubMed for the query “age-related hearing loss” for the 10-year period of 2006 to 2015. Manuscripts were included if they were original research (not reviews or meta-analyses), written in English, contained an abstract, used animals, and were primarily on age-related hearing loss. These criteria yielded 231 unique manuscripts for inclusion in the study analysis. The text of each manuscript was screened for the sex of the animals, the number of male and female animals, the discussion of sex-based results, the study site (US or international), and the year of publication. Results Only two thirds of manuscripts reported the sex of animals used in the experiments, and of these, 54% used both sexes, 34% used males only, and 13% used females only. In papers reporting sex and number of animals used, 67% were males and 33% were females. Over twice as many internationally based studies used males only compared to US-based studies. Only 15% of all manuscripts discussed sex-based results. Conclusions Sex bias is present in basic and preclinical age-related hearing loss research for the manuscripts screened in the 10-year period. Equal inclusion of both males and females in basic and preclinical age-related hearing loss research is critical for understanding sex-based differences in mechanisms and for effective treatment options

ROC for model adjusting for type of atrial arrhythmia.

ROC for model adjusting for type of atrial arrhythmia.</p

ROC for new-onset renal failure.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.</div

Coefficients of biomarker-only model for new-onset atrial arrhythmia.

Coefficients of biomarker-only model for new-onset atrial arrhythmia.</p

Univariate associations between variables and in-hospital mortality.

Univariate associations between variables and in-hospital mortality.</p

Performance for biomarkers-only model and full model for in-hospital mortality.

Performance for biomarkers-only model and full model for in-hospital mortality.</p

ROC for predictive models.

(A) In-hospital mortality: all biomarkers and measurable biomarkers; demographics (age, gender, race), medical history (diabetes mellitus, congestive heart failure, pulmonary Embolism, Malignancies), measurable biomarkers (BMI, LDH, Ferritin, Troponin, CPK, CRP, BNP, Cr, Lactate, K, Mg, Hb, SBP were used to predict the outcome In-hospital mortality. (B) Individual biomarkers; measurable biomarkers AUC≥0.7 was reported (C) New-onset AA: All biomarkers and measurable biomarkers; demographics (age, gender, race), medical history (diabetes mellitus, congestive heart failure, pulmonary Embolism, Malignancies), measurable biomarkers (BMI, LDH, Ferritin, Troponin, CPK, CRP, BNP, Cr, Lactate, K, Mg, Hb, SBP were used to predict the outcome new-onset AA. (D) Individual Biomarkers; measurable biomarkers AUC≥0.6 was reported.</p

ROC for ICU admission.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.</div

Baseline demographic, biomarker, and hospital event characteristics.

Baseline demographic, biomarker, and hospital event characteristics.</p