Association between intraoperative hypotension and postoperative nausea and vomiting: a retrospective cohort study.

OBJECTIVE: Postoperative nausea and vomiting (PONV) occurs in up to 30% of patients and its pathophysiology and mechanisms have not been completely described. Hypotension and a decrease in cardiac output are suspected to induce nausea. The hypothesis that intraoperative hypotension might influence the incidence of PONV was investigated. MATERIAL AND METHODS: The study was conducted as a retrospective large single center cohort study. The incidence of PONV was investigated until discharge from post anesthesia care unit (PACU). Surgical patients with general anesthesia during a 2-year period between 2018 and 2019 at a university hospital in Germany were included. Groups were defined based on the lowest documented mean arterial pressure (MAP) with group H50: MAP <50mmHg; group H60: MAP <60mmHg; group H70: MAP <70mmHg, and group H0: no MAP <70mmHg. Decreases of MAP in the different groups were related to PONV. Propensity-score matching was carried out to control for overlapping risk factors. RESULTS: In the 2-year period 18.674 patients fit the inclusion criteria. The overall incidence of PONV was 11%. Patients with hypotension had a significantly increased incidence of PONV (H0 vs. H50: 11.0% vs.17.4%, Risk Ratio (RR): 1.285 (99%CI: 1.102-1.498), p < 0.001; H0 vs. H60: 10.4% vs. 13.5%, RR: 1.1852 (99%CI: 1.0665-1.3172), p < 0.001; H0 vs. H70: 9.4% vs. 11.2%, RR: 1.1236 (99%CI: 1.013 - 1.2454); p = 0.0027). CONCLUSION: The study demonstrates an association between intraoperative hypotension and early PONV. A more severe decrease of MAP had a pronounced effect.

Addressing researcher degrees of freedom through minP adjustment.

When different researchers study the same research question using the same dataset they may obtain different and potentially even conflicting results. This is because there is often substantial flexibility in researchers' analytical choices, an issue also referred to as "researcher degrees of freedom". Combined with selective reporting of the smallest p-value or largest effect, researcher degrees of freedom may lead to an increased rate of false positive and overoptimistic results. In this paper, we address this issue by formalizing the multiplicity of analysis strategies as a multiple testing problem. As the test statistics of different analysis strategies are usually highly dependent, a naive approach such as the Bonferroni correction is inappropriate because it leads to an unacceptable loss of power. Instead, we propose using the "minP" adjustment method, which takes potential test dependencies into account and approximates the underlying null distribution of the minimal p-value through a permutation-based procedure. This procedure is known to achieve more power than simpler approaches while ensuring a weak control of the family-wise error rate. We illustrate our approach for addressing researcher degrees of freedom by applying it to a study on the impact of perioperative p a O 2 on post-operative complications after neurosurgery. A total of 48 analysis strategies are considered and adjusted using the minP procedure. This approach allows to selectively report the result of the analysis strategy yielding the most convincing evidence, while controlling the type 1 error-and thus the risk of publishing false positive results that may not be replicable.

Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning.

Weaning patients from mechanical ventilation (MV) is a critical and resource intensive process in the Intensive Care Unit (ICU) that impacts patient outcomes and healthcare expenses. Weaning methods vary widely among providers. Prolonged MV is associated with adverse events and higher healthcare expenses. Predicting weaning readiness is a non-trivial process in which the positive end-expiratory pressure (PEEP), a crucial component of MV, has potential to be indicative but has not yet been used as the target. We aimed to predict successful weaning from mechanical ventilation by targeting changes in the PEEP-level using a supervised machine learning model. This retrospective study included 12,153 mechanically ventilated patients from Medical Information Mart for Intensive Care (MIMIC-IV) and eICU collaborative research database (eICU-CRD). Two machine learning models (Extreme Gradient Boosting and Logistic Regression) were developed using a continuous PEEP reduction as target. The data is splitted into 80% as training set and 20% as test set. The model's predictive performance was reported using 95% confidence interval (CI), based on evaluation metrics such as area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), F1-Score, Recall, positive predictive value (PPV), and negative predictive value (NPV). The model's descriptive performance was reported as the variable ranking using SHAP (SHapley Additive exPlanations) algorithm. The best model achieved an AUROC of 0.84 (95% CI 0.83-0.85) and an AUPRC of 0.69 (95% CI 0.67-0.70) in predicting successful weaning based on the PEEP reduction. The model demonstrated a Recall of 0.85 (95% CI 0.84-0.86), F1-score of 0.86 (95% CI 0.85-0.87), PPV of 0.87 (95% CI 0.86-0.88), and NPV of 0.64 (95% CI 0.63-0.66). Most of the variables that SHAP algorithm ranked to be important correspond with clinical intuition, such as duration of MV, oxygen saturation (SaO2), PEEP, and Glasgow Coma Score (GCS) components. This study demonstrates the potential application of machine learning in predicting successful weaning from MV based on continuous PEEP reduction. The model's high PPV and moderate NPV suggest that it could be a useful tool to assist clinicians in making decisions regarding ventilator management.

Predicting Hypoxia Using Machine Learning: Systematic Review.

Background: Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration. Objective: This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population. Methods: A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm. Conclusions: Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.

Mechanical Power Density Predicts Prolonged Ventilation Following Double Lung Transplantation.

Prolonged mechanical ventilation (PMV) after lung transplantation poses several risks, including higher tracheostomy rates and increased in-hospital mortality. Mechanical power (MP) of artificial ventilation unifies the ventilatory variables that determine gas exchange and may be related to allograft function following transplant, affecting ventilator weaning. We retrospectively analyzed consecutive double lung transplant recipients at a national transplant center, ventilated through endotracheal tubes upon ICU admission, excluding those receiving extracorporeal support. MP and derived indexes assessed up to 36 h after transplant were correlated with invasive ventilation duration using Spearman's coefficient, and we conducted receiver operating characteristic (ROC) curve analysis to evaluate the accuracy in predicting PMV (>72 h), expressed as area under the ROC curve (AUROC). PMV occurred in 82 (35%) out of 237 cases. MP was significantly correlated with invasive ventilation duration (Spearman's ρ = 0.252 [95% CI 0.129-0.369], p < 0.01), with power density (MP normalized to lung-thorax compliance) demonstrating the strongest correlation (ρ = 0.452 [0.345-0.548], p < 0.01) and enhancing PMV prediction (AUROC 0.78 [95% CI 0.72-0.83], p < 0.01) compared to MP (AUROC 0.66 [0.60-0.72], p < 0.01). Mechanical power density may help identify patients at risk for PMV after double lung transplantation.

Retro-miRs: novel and functional miRNAs originating from mRNA retrotransposition.

BACKGROUND: Reverse-transcribed gene copies (retrocopies) have emerged as major sources of evolutionary novelty. MicroRNAs (miRNAs) are small and highly conserved RNA molecules that serve as key post-transcriptional regulators of gene expression. The origin and subsequent evolution of miRNAs have been addressed but not fully elucidated. RESULTS: In this study, we performed a comprehensive investigation of miRNA origination through retroduplicated mRNA sequences (retro-miRs). We identified 17 retro-miRs that emerged from the mRNA retrocopies. Four of these retro-miRs had de novo origins within retrocopied sequences, while 13 retro-miRNAs were located within exon regions and duplicated along with their host mRNAs. We found that retro-miRs were primate-specific, including five retro-miRs conserved among all primates and two human-specific retro-miRs. All retro-miRs were expressed, with predicted and experimentally validated target genes except miR-10527. Notably, the target genes of retro-miRs are involved in key biological processes such as metabolic processes, cell signaling, and regulation of neurotransmitters in the central nervous system. Additionally, we found that these retro-miRs play a potential oncogenic role in cancer by targeting key cancer genes and are overexpressed in several cancer types, including liver hepatocellular carcinoma and stomach adenocarcinoma. CONCLUSIONS: Our findings demonstrated that mRNA retrotransposition is a key mechanism for the generation of novel miRNAs (retro-miRs) in primates. These retro-miRs are expressed, conserved, have target genes with important cellular functions, and play important roles in cancer.

Towards interoperability in infection control: a standard data model for microbiology.

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages.

COVID-19 has been spreading widely since January 2020, prompting the implementation of non-pharmaceutical interventions and vaccinations to prevent overwhelming the healthcare system. Our study models four waves of the epidemic in Munich over two years using a deterministic, biology-based mathematical model of SEIR type that incorporates both non-pharmaceutical interventions and vaccinations. We analyzed incidence and hospitalization data from Munich hospitals and used a two-step approach to fit the model parameters: first, we modeled incidence without hospitalization, and then we extended the model to include hospitalization compartments using the previous estimates as a starting point. For the first two waves, changes in key parameters, such as contact reduction and increasing vaccinations, were enough to represent the data. For wave three, the introduction of vaccination compartments was essential. In wave four, reducing contacts and increasing vaccinations were critical parameters for controlling infections. The importance of hospitalization data was highlighted, as it should have been included as a crucial parameter from the outset, along with incidence, to avoid miscommunication with the public. The emergence of milder variants like Omicron and a significant proportion of vaccinated people has made this fact even more evident.

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.

The RAPID-score: Risk Assessment and PredIction of Delirium in acute stroke patients based on very early clinical parameters.

Background and objective: Post-stroke delirium (PSD) is a common complication in acute stroke patients, and guidelines recommend routine screening and various preventive and treatment measures. However, there is a substantial lack of standardized approaches in diagnostic and therapeutic management of PSD. Here, we aimed to develop a new pragmatic and easily assessable screening tool to predict PSD based on early parameters, which are already integral to acute stroke diagnostics. Methods: We enrolled acute stroke patients admitted to our stroke unit or intensive care unit and developed the scoring system using retrospective single-center patient data. The Confusion Assessment Method for the Intensive Care Unit was used for prospective score validation. Logistic regression models were employed to analyze the association of early clinical and paraclinical parameters with PSD development. Results: N = 525 patients (median age: 76 years; 45.7% female) were enrolled, with 29.7% developing PSD during hospitalization. The resulting score comprises 6 items, including medical history, clinical examination findings, and non-contrast computed tomography results at admission. Scores range from -15 to +15 points, with higher values indicating a higher likelihood of PSD, ranging from 4% to 79%. The accuracy was 0.85, and the area under the curve was 0.89. Conclusion: The new RAPID (Risk Assessment and PredIction of Delirium in acute stroke patients)-score shows high accuracy in predicting PSD among acute stroke patients and offers precise odds of PSD for each corresponding score value, utilizing routine early clinical and paraclinical parameters. It can identify high-risk populations for clinical study interventions and may be suitable to guide prophylactic PSD measures.

Corticotropin-stimulated steroid profiles to predict shock development and mortality in sepsis: From the HYPRESS study.

RATIONALE: Steroid profiles in combination with a corticotropin stimulation test provide information about steroidogenesis and its functional reserves in critically ill patients. OBJECTIVES: We investigated whether steroid profiles before and after corticotropin stimulation can predict the risk of in-hospital death in sepsis. METHODS: An exploratory data analysis of a double blind, randomized trial in sepsis (HYPRESS [HYdrocortisone for PRevention of Septic Shock]) was performed. The trial included adult patients with sepsis who were not in shock and were randomly assigned to placebo or hydrocortisone treatment. Corticotropin tests were performed in patients prior to randomization and in healthy subjects. Cortisol and precursors of glucocorticoids (17-OH-progesterone, 11-desoxycortisol) and mineralocorticoids (11-desoxycorticosterone, corticosterone) were analyzed using the multi-analyte stable isotope dilution method (LC-MS/MS). Measurement results from healthy subjects were used to determine reference ranges, and those from placebo patients to predict in-hospital mortality. MEASUREMENTS AND MAIN RESULTS: Corticotropin tests from 180 patients and 20 volunteers were included. Compared to healthy subjects, patients with sepsis had elevated levels of 11-desoxycorticosterone and 11-desoxycortisol, consistent with activation of both glucocorticoid and mineralocorticoid pathways. After stimulation with corticotropin, the cortisol response was subnormal in 12% and the corticosterone response in 50% of sepsis patients. In placebo patients (n = 90), a corticotropin-stimulated cortisol-to-corticosterone ratio > 32.2 predicted in-hospital mortality (AUC 0.8 CI 0.70-0.88; sensitivity 83%; and specificity 78%). This ratio also predicted risk of shock development and 90-day mortality. CONCLUSIONS: In this exploratory analysis, we found that in sepsis mineralocorticoid steroidogenesis was more frequently impaired than glucocorticoid steroidogenesis. The corticotropin-stimulated cortisol-to-corticosterone ratio predicts the risk of in-hospital death. Trial registration Clinical trial registered with www. CLINICALTRIALS: gov Identifier: NCT00670254. Registered 1 May 2008, https://clinicaltrials.gov/ct2/show/NCT00670254 .

Association between post-operative delirium and use of volatile anesthetics in the elderly: A real-world big data approach.

STUDY OBJECTIVE: Early post-operative delirium is a common perioperative complication in the post anesthesia care unit. To date it is unknown if a specific anesthetic regime can affect the incidence of delirium after surgery. Our objective was to examine the effect of volatile anesthetics on post-operative delirium. DESIGN: Single Center Observational Study. SETTING: Post Anesthesia Care Units at a German tertiary medical center. PATIENTS: 30,075 patients receiving general anesthesia for surgery. MEASUREMENTS: Delirium was assessed with the Nursing Delirium Screening Scale at the end of the recovery period. Subgroup-specific effects of volatile anesthetics on post-operative delirium were estimated using generalized-linear-model trees with inverse probability of treatment weighting. We further assessed the age-specific effect of volatiles using logistic regression models. MAIN RESULTS: Out of 30,075 records, 956 patients (3.2%) developed delirium in the post anesthesia care unit. On average, patients who developed delirium were older than patients without delirium. We found volatile anesthetics to increase the risk (Odds exp. (B) for delirium in the elderly 1.8-fold compared to total intravenous anesthesia. Odds increases with unplanned surgery 3.0-fold. In the very old (87 years or older), the increase in delirium is 6.2-fold. This result was confirmed with internal validation and in a logistic regression model. CONCLUSIONS: Our exploratory study indicates that early postoperative delirium is associated with the use of volatile anesthetics especially in the sub-cohort of patients aged 75 years and above. Further studies should include both volatile and intravenous anesthetics to find the ideal anesthetic in elderly patients.

A Functional Network Driven by MicroRNA-125a Regulates Monocyte Trafficking in Acute Inflammation.

During the onset of acute inflammation, rapid trafficking of leukocytes is essential to mount appropriate immune responses towards an inflammatory insult. Monocytes are especially indispensable for counteracting the inflammatory stimulus, neutralising the noxa and reconstituting tissue homeostasis. Thus, monocyte trafficking to the inflammatory sites needs to be precisely orchestrated. In this study, we identify a regulatory network driven by miR-125a that affects monocyte adhesion and chemotaxis by the direct targeting of two adhesion molecules, i.e., junction adhesion molecule A (JAM-A), junction adhesion molecule-like (JAM-L) and the chemotaxis-mediating chemokine receptor CCR2. By investigating monocytes isolated from patients undergoing cardiac surgery, we found that acute yet sterile inflammation reduces miR-125a levels, concomitantly enhancing the expression of JAM-A, JAM-L and CCR2. In contrast, TLR-4-specific stimulation with the pathogen-associated molecular pattern (PAMP) LPS, usually present within the perivascular inflamed area, resulted in dramatically induced levels of miR-125a with concomitant repression of JAM-A, JAM-L and CCR2 as early as 3.5 h. Our study identifies miR-125a as an important regulator of monocyte trafficking and shows that the phenotype of human monocytes is strongly influenced by this miRNA, depending on the type of inflammatory stimulus.

Early Use of Methylene Blue in Vasoplegic Syndrome: A 10-Year Propensity Score-Matched Cohort Study.

BACKGROUND: Vasoplegic syndrome is associated with increased morbidity and mortality in patients undergoing cardiac surgery. This retrospective, single-center study aimed to evaluate the effect of early use of methylene blue (MB) on hemodynamics after an intraoperative diagnosis of vasoplegic syndrome (VS). METHODS: Over a 10-year period, all patients diagnosed with intraoperative VS (hypotension despite treatment with norepinephrine ≥0.3 µg/kg/min and vasopressin ≥1 IE/h) while undergoing heart surgery and cardiopulmonary bypass were identified, and their data were examined. The intervention group received MB (2 mg/kg intravenous) within 15 min after the diagnosis of vasoplegia, while the control group received standard therapy. The two groups were matched using propensity scores. RESULTS: Of the 1022 patients identified with VS, 221 received MB intraoperatively, and among them, 60 patients received MB within 15 min after the diagnosis of VS. After early MB application, mean arterial pressure was significantly higher, and vasopressor support was significantly lower within the first hour (p = 0.015) after the diagnosis of vasoplegia, resulting in a lower cumulative amount of norepinephrine (p = 0.018) and vasopressin (p = 0.003). The intraoperative need of fresh frozen plasma in the intervention group was lower compared to the control group (p = 0.015). Additionally, the intervention group had higher creatinine values in the first three postoperative days (p = 0.036) without changes in dialysis incidence. The 90-day survival did not differ significantly (p = 0.270). CONCLUSION: Our results indicate the additive effects of MB use during VS compared to standard vasopressor therapy only. Early MB administration for VS may significantly improve the patients' hemodynamics with minor side effects.

[SEPSIS-3.0-Is intensive care medicine ready for ICD-11?] / SEPSIS-3.0 ­ Ist die Intensivmedizin bereit für die ICD-11?

BACKGROUND: The 11th revision of the International Classification of Diseases (ICD-11) will come into effect in January 2022. Among other things, The Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS­3 definition) will be implemented in it. This defines sepsis as a "life-threatening organ dysfunction caused by a dysregulated host response to infection". The aim of the present secondary analysis of a survey on the topic of "sepsis-induced coagulopathy" was to evaluate whether the SEPSIS­3 definition, 4 years after its international introduction, has arrived in everyday clinical practice of intensive care units (ICU) run by anesthesiologists in Germany and thus the requirements for its use of the ICD-11 are given. METHODS: Between October 2019 and May 2020, we carried out a nationwide survey among German medical directors of ICUs. In a separate block of questions we asked about the definition of sepsis used in daily practice. In addition, we asked whether the quick-sequential (sepsis-related) organ failure assessment (qSOFA) score is used in screening for sepsis in the hospital to which to the participating ICU belongs. RESULTS: A total of 50 medical directors from anesthesiological ICUs took part in the survey. In total, the ICUs evaluated stated that they had around 14% of the high-care beds registered in Germany. The SEPSIS­3 definition is integrated into everyday clinical practice at 78.9% of the university hospitals and 84.0% of the participating teaching hospitals. In contrast, the qSOFA screening test is only used by 26.3% of the participating university hospitals, but at least 52% of the teaching hospitals and 80% of the other hospitals. CONCLUSION: The data show that both SEPSIS­3 and qSOFA have become part of everyday clinical practice in German hospitals. The cautious use of qSOFA at university hospitals with simultaneous broad acceptance of the SEPSIS­3 definition can be interpreted as an indication that the search for a suitable screening test for sepsis has not yet been completed.

[Practice of pharmaceutical thrombosis prophylaxis and anticoagulation in patients with sepsis and pre-existing anticoagulation or heparin-induced type II thrombocytopenia-Results of a nationwide survey in German intensive care units]. / Praxis der medikamentösen Thromboseprophylaxe und Antikoagulation bei Patienten mit Sepsis und vorbestehender Antikoagulation oder Heparin-induzierter Thrombozytopenie Typ II ­ Ergebnisse einer deutschlandweiten Umfrage auf Intensivstationen.

BACKGROUND: A pre-existing anticoagulation treatment and predisposing diseases for thromboembolic events represent common problems in patients with sepsis or septic shock; however, these conditions are not addressed in current national guidelines for sepsis and septic shock. One of the aims of this nationwide survey in Germany was therefore to determine how intensive care physicians deal with these problems. METHODS: From October 2019 to May 2020, we conducted a nationwide survey among German medical directors of intensive care units (ICU) addressing anticoagulation and drug-based prophylaxis of venous thromboembolism (VTE) in patients with sepsis and sepsis-induced coagulopathy. One focus was the procedure for patients with a pre-existing anticoagulation treatment or a previously known heparin-induced thrombocytopenia (HIT) type 2 (acute symptomatic vs. dating back years). RESULTS: In most of the participating ICUs pre-existing anticoagulation is largely continued with low molecular weight heparin preparations or unfractionated heparin. In patients with pre-existing HIT type 2 both acute symptomatic and dating back years, argatroban represents the drug of choice. There is a high degree of variability in the definition of the target values, usually being well above the range for pure VTE prophylaxis. CONCLUSION: Data on the continuation of anticoagulation beyond VTE prophylaxis with a subsequently increased risk of bleeding in patients with sepsis and septic shock is limited and treatment decisions are in many cases subject to individual consideration by the practitioner. The results of our survey imply the need for a systematic work-up of this topic in order to support daily practice in many ICUs with the required evidence.

Anthropometric analysis of body habitus and outcomes in critically ill COVID-19 patients.

AIMS: This study aimed to determine whether anthropometric markers of thoracic skeletal muscle and abdominal visceral fat tissue correlate with outcome parameters in critically ill COVID-19 patients. METHODS: We retrospectively analysed thoracic CT-scans of 67 patients in four ICUs at a university hospital. Thoracic skeletal muscle (total cross-sectional area (CSA); pectoralis muscle area (PMA)) and abdominal visceral fat tissue (VAT) were quantified using a semi-automated method. Point-biserial-correlation-coefficient, Spearman-correlation-coefficient, Wilcoxon rank-sum test and logistic regression were used to assess the correlation and test for differences between anthropometric parameters and death, ventilator- and ICU-free days and initial inflammatory laboratory values. RESULTS: Deceased patients had lower CSA and PMA values, but higher VAT values (p < 0.001). Male patients with higher CSA values had more ventilator-free days (p = 0.047) and ICU-free days (p = 0.017). Higher VAT/CSA and VAT/PMA values were associated with higher mortality (p < 0.001), but were negatively correlated with ICU length of stay in female patients only (p < 0.016). There was no association between anthropometric parameters and initial inflammatory biomarker levels. Logistic regression revealed no significant independent predictor for death. CONCLUSION: Our study suggests that pathologic body composition assessed by planimetric measurements using thoracic CT-scans is associated with worse outcome in critically ill COVID-19 patients.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.