Prior fluid and electrolyte imbalance is associated with COVID-19 mortality

Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU

Bayesian approaches have been utilized to address the challenge of variable selection and statistical inference in high-dimensional survival analysis. However, the discontinuity of the ℓ 0 -norm prior, including the useful spike-and-slab prior, may lead to computational and implementation challenges, potentially limiting the widespread use of Bayesian methods. The Gaussian and diffused-gamma (GD) prior has emerged as a promising alternative due to its continuous-and-differentiable ℓ 0 -norm approximation and computational efficiency in generalized linear models. In this paper, we extend the GD prior to semi-parametric Cox models by proposing a rank-based Bayesian inference procedure with the Cox partial likelihood. We develop a computationally efficient algorithm based on the iterative conditional mode (ICM) and Markov chain Monte Carlo methods for posterior inference. Our simulations demonstrate the effectiveness of the proposed method, and we apply it to an electronic health record dataset to identify risk factors associated with COVID-19 mortality in ICU patients at a regional medical center.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Negative prognostic impact of electrolyte disorders in patients hospitalized for Covid-19 in a large multicenter study

BACKGROUND

The prognostic impact of electrolyte disorders in hospitalized COVID-19 patients is unclear.

METHODS

The study included all adult patients hospitalized for COVID-19 in four hospitals in Northern Italy between January 2020 and May 2021 with at least one serum potassium and sodium measurement performed within 3 days since admission. Primary outcome was in-hospital death; secondary outcome was Intensive Care Unit (ICU) admission. A cause-specific Cox proportional-hazards regression model was used for investigating the association between potassium and sodium (as either categorical or continuous variables) and mortality or admission to ICU.

RESULTS

Analyses included 3,418 adult hospitalized COVID-19 patients. At multivariable analysis, both hyperkalemia (Hazard Ratio, [HR] 1.833, 95% Confidence Interval [CI] 1.371-2.450) and sK above the median (K 5.1 vs 4.1 mmol/L: HR 1.523, 95% CI 1.295-1.798), and hypernatremia (HR 2.313, 95%CI 1.772-3.018) and sNa above the median (Na 149 vs 139 mmol/L: HR 1.442, 95% CI 1.234-1.686), were associated with in-hospital death, whereas hypokalemia and hyponatremia were not. Hyponatremia was associated with increased hazard of ICU admission (HR 1.884, 95%CI 1.389-2.556).

CONCLUSIONS

Electrolyte disorders detected at hospital admission may allow early identification of COVID-19 patients at increased risk of adverse outcomes.

Patient and ward related risk factors in a multi-ward nosocomial outbreak of COVID-19: Outbreak investigation and matched case-control study

BACKGROUND

Risk factors for nosocomial COVID-19 outbreaks continue to evolve. The aim of this study was to investigate a multi-ward nosocomial outbreak of COVID-19 between 1st September and 15th November 2020, occurring in a setting without vaccination for any healthcare workers or patients.

METHODS

Outbreak report and retrospective, matched case-control study using incidence density sampling in three cardiac wards in an 1100-bed tertiary teaching hospital in Calgary, Alberta, Canada. Patients were confirmed/probable COVID-19 cases and contemporaneous control patients without COVID-19. COVID-19 outbreak definitions were based on Public Health guidelines. Clinical and environmental specimens were tested by RT-PCR and as applicable quantitative viral cultures and whole genome sequencing were conducted. Controls were inpatients on the cardiac wards during the study period confirmed to be without COVID-19, matched to outbreak cases by time of symptom onset dates, age within ± 15 years and were admitted in hospital for at least 2 days. Demographics, Braden Score, baseline medications, laboratory measures, co-morbidities, and hospitalization characteristics were collected on cases and controls. Univariate and multivariate conditional logistical regression was used to identify independent risk factors for nosocomial COVID-19.

RESULTS

The outbreak involved 42 healthcare workers and 39 patients. The strongest independent risk factor for nosocomial COVID-19 (IRR 3.21, 95% CI 1.47-7.02) was exposure in a multi-bedded room. Of 45 strains successfully sequenced, 44 (97.8%) were B.1.128 and differed from the most common circulating community lineages. SARS-CoV-2 positive cultures were detected in 56.7% (34/60) of clinical and environmental specimens. The multidisciplinary outbreak team observed eleven contributing events to transmission during the outbreak.

CONCLUSIONS

Transmission routes of SARS-CoV-2 in hospital outbreaks are complex; however multi-bedded rooms play a significant role in the transmission of SARS-CoV-2.

Wearable chemical sensors for biomarker discovery in the omics era

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

Electrolyte Imbalance and COVID-19 Severity in Hospitalized Patients

Background: Various risk factors have been proposed for severe coronavirus disease 2019 (COVID-19); nonetheless, the prognostic role of serum electrolytes has not been widely studied. Objectives: The present study aimed to identify the potential prognostic role of electrolyte imbalance in hospitalized COVID-19 patients. Methods: This retrospective study was conducted in Imam Reza Hospital, Mashhad, Iran. The medical records of all COVID-19 patients admitted to the emergency department from May to August 2020 were evaluated. Demographic data and clinical findings upon admission were collected. Disease severity, lung involvement severity on imaging, inflammatory serum biomarkers, admission to the intensive care unit, and serum levels of sodium, potassium, magnesium, calcium (corrected by serum albumin level), and phosphorus were documented. Results: Most patients (60%) were male, and the mean age of the total population was 58.87 ± 1.82 years. Severe COVID-19 was detected in most cases (94.9%) who were significantly older (P = 0.037), had hypertension (P = 0.032), ischemic heart disease (P = 0.033), and higher serum urea (P = 0.001) and serum potassium (P < 0.001). Patients with poor prognosis based on computed tomography (CT) scores had significantly higher serum urea (P = 0.002) and magnesium (P = 0.035) than patients with good prognosis, while serum calcium was significantly higher in the latter group (P = 0.007). Furthermore, there was a significant relationship between COVID-19 severity and serum potassium (P < 0.001). Conclusions: Abnormal serum electrolytes are correlated with COVID-19 severity. Moreover, serum potassium level is a predictor of severe disease.

Hypo- and Hyperphosphatemia at Admission as Independent Factors of Mortality of COVID-19 Patients: Findings from a Retrospective Cohort Study

BACKGROUND

Electrolyte imbalances are common in COVID-19 infection and are associated with poor outcomes in hospitalized patients.

OBJECTIVES

This study examined whether serum phosphate imbalances at admission are associated with mortality in hospitalized COVID-19 patients.

METHODS

In this registry-based single-center retrospective cohort study, 1349 inpatients with COVID-19 were included from March 2020 to March 2021 in an academic hospital in Ilam (southwest Iran). The Cox proportional hazard (PH) regression model was applied to the data set of COVID-19.

RESULTS

The in-hospital median survival time for patients with low, normal, and high serum phosphate levels was 14, 25, and 8 days, respectively. In a multivariate model, adjusted for the other variables, patients with hypophosphatemia (adjusted hazard ratio [HR], 2.53; 95% CI, 1.15 - 5.58; P = 0.02) and hyperphosphatemia (adjusted HR, 1.77; 95% CI, 1.00 - 3.14; P = 0.05) had an increased mortality hazard compared with those who had normal levels of serum phosphate.

CONCLUSIONS

Our results demonstrate associations of hypophosphatemia and hyperphosphatemia with increased in-hospital mortality in COVID-19 patients. Intensive medical care and more attention must be paid to COVID-19 patients with serum phosphate imbalances at admission.