Risk of subsequent SARS-CoV-2 infection among vaccinated employees with or without hybrid immunity acquired early in the Omicron-predominant era of the COVID-19 pandemic.

BACKGROUND: Hybrid immunity, from COVID-19 vaccination followed by SARS-CoV-2 infection acquired after its Omicron variant began predominating, has provided greater protection than vaccination alone against subsequent infection over 1-3 months of observation. Its longer-term protection is unknown. METHODS: We conducted a retrospective cohort study of COVID-19 case incidence among healthcare personnel (HCP) mandated to be vaccinated and report on COVID-19-associated symptoms, high-risk exposures, or known-positive test results to an employee health hotline. We compared cases with hybrid immunity, defined as incident COVID-19 during the first 6 weeks of Omicron-variant predominance (run-in period), to those with immunity from vaccination alone during the run-in period. Time until COVID-19 infection over 13 subsequent months (observation period) was analyzed by standard survival analysis. RESULTS: Of 5867 employees, 641 (10.9%, 95% confidence interval [CI]: 10.1%-11.8%) acquired hybrid immunity during the run-in period. Of these, 104 (16.2%, 95% CI: 13.5%-19.3%) experienced new SARS-CoV-2 infection during the 13-month observation period, compared to 2177 (41.7%, 95% CI: 40.3%-43.0%) of the 5226 HCP without hybrid immunity. Time until incident infection was shorter among the latter (hazard ratio: 3.09, 95% CI: 2.54-3.78). CONCLUSIONS: In a cohort of vaccinated employees, Omicron-era acquired SARS-CoV-2 hybrid immunity was associated with significantly lower risk of subsequent infection over more than a year of observation-a time period far longer than previously reported and during which three, progressively more resistant, Omicron subvariants became predominant. These findings can inform institutional policy and planning for future COVID-19 additional vaccine dosing requirements for employees, for surveillance programs, and for risk modification efforts.

Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust.

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.

Prevention of Occupational Heat-Related Illnesses.

High ambient temperatures and strenuous physical activity put workers at risk for a variety of heat-related illnesses and injuries. Through primary prevention, secondary prevention, and treatment, OEM health providers can protect workers from the adverse effects of heat. This statement by the American College of Occupational and Environmental Medicine provides guidance for OEM providers who serve workers and employers in industries where heat exposure occurs.

Temporal Trends in COVID-19 Incidence in Two Healthcare Worker Cohorts.

BACKGROUND: Health care workers (HCWs) experience increased occupational risk of contracting COVID-19, with temporal trends that might inform surveillance. METHODS: We analyzed data from a Veterans Affairs hospital-based COVID-19 worker telephone hotline collected over 40 weeks (2020). We calculated the proportion of COVID-19+ cases among persons-under-investigation (PUIs) for illness compared to rates from a nearby large university-based health care institution. RESULTS: We observed 740 PUIs, 65 (8.8%) COVID-19+. Time trends were similar at the study and comparison hospitals; only for the first of 10 four-week observation periods was the ratio for observed to expected COVID-19+ significant (P < 0.001). DISCUSSION: These data suggest that employee health COVID-19+ to PUI ratios could be utilized as a barometer of community trends. Pooling experience among heath care facilities may yield insights into occupational infectious disease outbreaks.

Predicting hospital mortality for intensive care unit patients: Time-series analysis.

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients.

BACKGROUND: The National Early Warning Score (NEWS/NEWS 2) has been adopted across the National Health Service (NHS) in the U.K. as a method of escalating care for deteriorating patients. Intensive Care Unit (ICU) resources are limited and in high demand, with patient discharge a focal point for managing resources effectively. There are currently no universally accepted methods for assessing discharge of patients from an ICU, which can cause premature discharges and put patients at risk of subsequent deterioration, readmission to ICU or death. METHODS: We tested the ability of the NEWS to discriminate patients within 24h of admission to an ICU in a U.S. hospital during 2001-2012, by their end discharge location: home; hospital ward; nursing facility; hospice and death. The NEWS performance was compared across five different ICU specialties, using the area under the receiver operating characteristic (AUROC) curve and a large vital signs database (n=2,723,055) collected from 28,523 critical care admissions. RESULTS: The NEWS AUROC (95% CI) at 24h following admission: all patients 0.727 (0.709-0.745); Coronary Care Unit (CCU) 0.829 (0.821-0.837); Cardiac Surgery Recovery Unit (CSRU) 0.844 (0.838-0.850); Medical Intensive Care Unit (MICU) 0.778 (0.767-0.791); Surgical Intensive Care Unit (SICU) 0.775 (0.762-0.788); Trauma Surgical Intensive Care Unit (TSICU) 0.765 (0.751-0.773). CONCLUSIONS: The NEWS has reasonable discrimination for any ICU patient's discharge location. The NEWS has greater ability to discriminate patients in the Coronary Care Unit (CCU) and Cardiac Surgery Recovery Unit (CSRU) compared to other ICU specialties. The NEWS has the real potential to be applied within a universal discharge planning tool for ICU, improving patient safety at the point of discharge.

Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach.

BACKGROUND: Mortality prediction of hospitalized patients is an important problem. Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality. By contrast, early mortality prediction for intensive care unit patients remains an open challenge. Most research has focused on severity of illness scoring systems or data mining (DM) models designed for risk estimation at least 24 or 48h after ICU admission. OBJECTIVES: This study highlights the main data challenges in early mortality prediction in ICU patients and introduces a new machine learning based framework for Early Mortality Prediction for Intensive Care Unit patients (EMPICU). MATERIALS AND METHODS: The proposed method is evaluated on the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database. Mortality prediction models are developed for patients at the age of 16 or above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU). We employ the ensemble learning Random Forest (RF), the predictive Decision Trees (DT), the probabilistic Naive Bayes (NB) and the rule-based Projective Adaptive Resonance Theory (PART) models. The primary outcome was hospital mortality. The explanatory variables included demographic, physiological, vital signs and laboratory test variables. Performance measures were calculated using cross-validated area under the receiver operating characteristic curve (AUROC) to minimize bias. 11,722 patients with single ICU stays are considered. Only patients at the age of 16 years old and above in Medical ICU (MICU), Surgical ICU (SICU) or Cardiac Surgery Recovery Unit (CSRU) are considered in this study. RESULTS: The proposed EMPICU framework outperformed standard scoring systems (SOFA, SAPS-I, APACHE-II, NEWS and qSOFA) in terms of AUROC and time (i.e. at 6h compared to 48h or more after admission). DISCUSSION AND CONCLUSION: The results show that although there are many values missing in the first few hour of ICU admission, there is enough signal to effectively predict mortality during the first 6h of admission. The proposed framework, in particular the one that uses the ensemble learning approach - EMPICU Random Forest (EMPICU-RF) offers a base to construct an effective and novel mortality prediction model in the early hours of an ICU patient admission, with an improved performance profile.

Patient length of stay and mortality prediction: A survey.

Over the past few years, there has been increased interest in data mining and machine learning methods to improve hospital performance, in particular hospitals want to improve their intensive care unit statistics by reducing the number of patients dying inside the intensive care unit. Research has focused on prediction of measurable outcomes, including risk of complications, mortality and length of hospital stay. The length of stay is an important metric both for healthcare providers and patients, influenced by numerous factors. In particular, the length of stay in critical care is of great significance, both to patient experience and the cost of care, and is influenced by factors specific to the highly complex environment of the intensive care unit. The length of stay is often used as a surrogate for other outcomes, where those outcomes cannot be measured; for example as a surrogate for hospital or intensive care unit mortality. The length of stay is also a parameter, which has been used to identify the severity of illnesses and healthcare resource utilisation. This paper examines a range of length of stay and mortality prediction applications in acute medicine and the critical care unit. It also focuses on the methods of analysing length of stay and mortality prediction. Moreover, the paper provides a classification and evaluation for the analytical methods of the length of stay and mortality prediction associated with a grouping of relevant research papers published in the years 1984 to 2016 related to the domain of survival analysis. In addition, the paper highlights some of the gaps and challenges of the domain.

Coma query cause.

A 54-year-old woman with coeliac disease was admitted to hospital electively for supplemental nutrition. Shortly after feeding started she deteriorated into a hyperammonemic coma with refeeding syndrome, requiring an extensive intensive care admission. Urea cycle disorders were investigated and a biochemical diagnosis of ornithine transcarbamylase deficiency was made. This is a rare diagnosis in the adult population. This case report summarises protein metabolism, urea cycle disorders and the challenges of management.