Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashionthe first description and evaluation of this strategyand introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two GC × GC data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.
SARS-COV-2 (COVID-19) is a novel virus that has caused over 28 million cases worldwide and over 900,000 deaths since early 2020, rightfully being classified as a pandemic. COVID-19 is diagnosed via polymerase chain reaction testing which looks at cycle threshold (CT) values of two genes, N2 and E. This study examined CT values of COVID-positive patients at the VA hospital in Reno as well as other lab values and comorbidities to determine if any could aid clinicians in predicting the need for hospitalization and higher levels of care. Multiple variables, including N2 CT value, absolute lymphocyte count (ALC), D-dimer, erythrocyte sedimentation rate, C-reactive protein, fibrinogen, and ferritin were evaluated for potential associations with N2 CT value as well as required level of care (based on World Health Organization [WHO] ordinal score). The results suggest that patients with a N2 CT value less than 34 are four times more likely to have WHO ordinal scores of 4-8 (p = .0021) while controlling for age and comorbidities (DM, cardiac, kidney, and lung disease). Patients of age 55 or greater were 15.18 times more likely to have WHO ordinal scores of 4-8 (p = .012) controlling for N2 CT value and comorbidities. Furthermore, patients with ALC less than 1 were 5.88 times more likely to have WHO ordinal score of 4-8 (p = .00024). N2 CT values also appear to be associated with many commonly obtained markers such as ALC, white blood cell count, C-reactive protein, and D-dimer. Patients with N2 CT values less than 34 were 3.49 times more likely to have ALC values less than 1, controlling for age and comorbidities (p = .0072) while patients 55 or older were 6.66 times more likely to have ALC less than 1 (p = .027). Finally, this study confirms previous conclusions that patients with advanced age had more severe infections and thus will likely require higher levels of care.
Background Sepsis is marked by elevated histamine, which is a vasodilator that increases vascular permeability. Although human studies are lacking, murine models of sepsis have indicated potential protective effects of histamine 2 receptor antagonist administration (H2RAs). Objective To assess any association between H2RA use in sepsis-3 patients admitted to the ICU and mortality, mechanical ventilation, length of stay, and markers of renal, liver, and lung dysfunction. Design Retrospective cohort study. Setting Intensive care units of the Beth Israel Deaconess Medical Center (BIDMC) accessed via the MIMIC-IV database spanning an 11-year period from 2008 to 2019. Patients (or participants) A total of 30,591 patients met the inclusion criteria for sepsis-3 on admission (mean age 66.49, standard deviation 15.92). Main measures We collected patient age, gender, ethnicity, comorbidities (contained within the Charlson comorbidity index), SOFA score, OASIS score, APS III score, SAPS II score, H2RA use, creatinine, BUN, ALT, AST, and P/F ratios. Primary outcomes were mortality, mechanical ventilation, and ICU length of stay. Key results A total of 30,591 patients met inclusion criteria over the 11-year sample period. The 28-day in hospital mortality rate was significantly lower among patients who received an H2RA (12.6% vs 15.1%, p < 0.001) as compared to those who did not receive an H2RA. Patients receiving an H2RA had significantly lower adjusted odds of mortality (0.802, 95% CI 0.741–0.869, p < 0.001), but significantly higher adjusted odds of invasive mechanical ventilation (4.426, 95% CI 4.132–4.741, p < 0.001) and significantly higher ICU LOS (3.2 days vs. 2.4 days, p < 0.001) as compared to the non-H2RA group. H2RA use was also associated with decreased severity of acute respiratory distress syndrome (ARDS) and lower serum creatinine. Conclusion Among patients hospitalized in the ICU for sepsis, the use of an H2RA was associated with significantly lower odds of mortality, decreased severity of ARDS, and a lower incidence of renal insufficiency.
Acute respiratory distress syndrome (ARDS) is a severe complication of coronavirus disease 2019 (COVID-19) infection marked by increased fluid diffusely in alveolar spaces. The management of ARDS can be complicated by mechanical hyperinflation, and thus a mainstay of treatment has included low tidal volume mechanical ventilation. This, however, can lead to ventilation-associated hypercapnia, which may result in respiratory acidosis. COVID-19-associated ARDS (CARDs) has been described in the literature, and guidelines tend to mimic ARDS management. However, the heterogeneous nature of COVID-19 pulmonary disease with respect to dead space, compliance, and shunting could alter guidelines. As low tidal volume remains a cornerstone in CARDS management, hypercapnic acidosis remains a risk. An emerging technology, extracorporeal CO2 removal devices (ECCO2R), has been granted emergency use authorization by the FDA for the management of CARDS. We present a 44-year-old male patient presenting positive for COVID-19. Following admission, the patient's oxygen status continually deteriorated and the patient went into acute respiratory distress, eventually requiring invasive mechanical ventilation. The patient became hypercapnic and acidotic due to low tidal volume ventilation. ECCO2R was used to manage the patient's hypercapnia, resulting in significant amelioration of his partial pressure of carbon dioxide (PCO2) and pH. The patient was eventually transferred to extracorporeal membrane oxygenation (ECMO) certified facility and survived after a prolonged hospital and rehabilitation course. In the management of CARDS patients who require mechanical respiration, there are many unanswered questions as to the appropriate ventilation strategy. Current practice recommends low tidal volume ventilation, carrying, and increased risk of hypercapnic respiratory acidosis as occurred in our patient. We believe that ECCO2R may be an appropriate bridge between low tidal volume ventilation and ECMO to stabilize acid-base disturbances in ventilated patients.
Missing data is a significant issue in metabolomics that is often neglected when conducting data pre-processing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatog-raphy (GC×GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion—the first description and evaluation of this strategy—and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approach-es (Bayesian principal components analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.
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