Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19.

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (ie, death and/or intensive care unit admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, chest radiographic, and laboratory findings. Distribution and severity of lung involvement were visually assessed by using an eight-point scale (chest radiography score). Internal validation was performed by using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve. Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (mean age, 69 years ± 12 [standard deviation]; 237 men) of whom 168 (47%) developed critical illness. The final risk model's variables included sex, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease, and chest radiography score at hospital presentation. The area under the receiver operating characteristic curve of the model was 0.77 (95% CI: 0.72, 0.81; P < .001). A risk calculator was derived for individual risk assessment: Dutch COVID-19 risk model. At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.

Predictors of bleeding complications during catHeter-dirEcted thrombolysis for peripheral arterial occlusions (POCHET).

INTRODUCTION: The risk of major bleeding complications in catheter directed thrombolysis (CDT) for acute limb ischemia (ALI) remains high, with reported major bleeding complication rates in up to 1 in every 10 treated patients. Fibrinogen was the only predictive marker used for bleeding complications in CDT, despite the lack of high quality evidence to support this. Therefore, recent international guidelines recommend against the use of fibrinogen during CDT. However, no alternative biomarkers exist to effectively predict CDT-related bleeding complications. The aim of the POCHET biobank is to prospectively assess the rate and etiology of bleeding complications during CDT and to provide a biobank of blood samples to investigate potential novel biomarkers to predict bleeding complications during CDT. METHODS: The POCHET biobank is a multicentre prospective biobank. After informed consent, all consecutive patients with lower extremity ALI eligible for CDT are included. All patients are treated according to a predefined standard operating procedure which is aligned in all participating centres. Baseline and follow-up data are collected. Prior to CDT and subsequently every six hours, venous blood samples are obtained and stored in the biobank for future analyses. The primary outcome is the occurrence of non-access related major bleeding complications, which is assessed by an independent adjudication committee. Secondary outcomes are non-major bleeding complications and other CDT related complications. Proposed biomarkers to be investigated include fibrinogen, to end the debate on its usefulness, anti-plasmin and D-Dimer. DISCUSSION AND CONCLUSION: The POCHET biobank provides contemporary data and outcomes of patients during CDT for ALI, coupled with their blood samples taken prior and during CDT. Thereby, the POCHET biobank is a real world monitor on biomarkers during CDT, supporting a broad spectrum of future research for the identification of patients at high risk for bleeding complications during CDT and to identify new biomarkers to enhance safety in CDT treatment.

[A woman in acute respiratory distress]. / Een vrouw in acute ademnood.

A 59-year-old woman presented with acute respiratory distress and a superior vena cava syndrome. Her complaints were initiated by esophageal food obstruction. A CT-scan revealed a massively dilated esophagus caused by achalasia. There was compression of the trachea and the large veins of the neck. Patient underwent esophageal resection and gastric tube reconstruction.