Paediatric pneumonia: deriving a model to identify severe disease

2021 ◽  
pp. archdischild-2021-322665
Author(s):  
Stuart Haggie ◽  
Elizabeth H Barnes ◽  
Hiran Selvadurai ◽  
Hasantha Gunasekera ◽  
Dominic A Fitzgerald
Keyword(s):  
Intensive Care ◽  
Work Of Breathing ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Community Acquired Pneumonia ◽  
Sensitivity Analyses ◽  
Intensive Care Treatment ◽  
Clinical Tool ◽  
Clinical Variables ◽  
Roc Area

BackgroundCommunity-acquired pneumonia (CAP) is a leading cause of childhood hospitalisation. Limited data exist on factors predicting severe disease with no paediatric-specific predictive tools.MethodsRetrospective cohort (2011–2016) of hospitalised CAP cases. We analysed clinical variables collected at hospital presentation against outcomes. Stratified outcomes were mild (hospitalised), moderate (invasive drainage procedure, intensive care) or severe (mechanical ventilation, vasopressors, death).ResultsWe report 3330 CAP cases, median age 2.0 years (IQR 1–5 years), with 2950 (88.5%) mild, 305 (9.2%) moderate and 75 (2.3%) severe outcomes. Moderate-severe outcomes were associated with hypoxia (SaO2 <90%; OR 6.6, 95% CI 5.1 to 8.5), increased work of breathing (severe vs normal OR 5.8, 95% CI 4.2 to 8.0), comorbidities (4+ comorbidities vs nil; OR 8.8, 95% CI 5.5 to 14) and being indigenous (OR 4.7, 95% CI 2.6 to 8.4). Febrile children were less likely than afebrile children to have moderate-severe outcomes (OR 0.57 95% CI 0.44 to 0.74). The full model receiver operating characteristic (ROC) area under the curve (AUC) was 0.78. Sensitivity analyses showed similar results with clinical or radiological CAP definitions. We derived a clinical tool to stratify low, intermediate or high likelihood of severe disease (AUC 0.72). High scores (≥5) had nearly eight times higher odds of moderate-severe disease than those with a low (≤1) score (OR 7.7 95% CI 5.6 to 10.5).ConclusionsA clinical risk prediction tool is needed for child CAP. We have identified risk factors and derived a simple clinical tool using clinical variables at hospital presentation to determine a child’s risk of invasive or intensive care treatment with an ROC AUC comparable with adult pneumonia tools.

scholarly journals A tool to distinguish viral from bacterial pneumonia

2021 ◽  
Author(s):  
Alfredo Tagarro ◽  
Cinta Moraleda ◽  
Sara Dominguez-Rodriguez ◽  
Mario Jose Rodriguez ◽  
Maria Dolores Martin ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Community Acquired Pneumonia ◽  
Hospitalized Children ◽  
Training Set ◽  
Etiological Diagnosis ◽  
Choice Of Treatment ◽  
Roc Area ◽  
Atypical Bacteria

Establishing the etiology of community-acquired pneumonia (CAP) in children at admission is challenging. As a result, most children receive antibiotics that do not need. This study aims to build and validate a diagnostic tool combining clinical, analytical and radiographical features to sequentially differentiate viral from bacterial CAP, and among bacterial CAP, typical from atypical bacteria, to improve choice of treatment. Methods Consecutive hospitalized children between 1 month and 16 years of age with CAP were enrolled. An extensive microbiological workup was performed. A score was built with a training set of 70% patients, to first differentiate between viral and bacterial CAP and secondly, typical from atypical bacterial CAP. To select variables, a Ridge model was used. Optimal cut-off points were selected to maximize specificity setting a high sensitivity (80%). Weights of each variable were calculated with a multivariable logistic regression. The score was validated with the rest of the participants. Results In total, 262 (53%) children (median age, 2 years, 52.3% male) had an etiological diagnosis. The step 1 discriminates viral from bacterial CAP. Bacterial CAPs were classified with a sensitivity=97%, a specificity=48%, and a ROC area under the curve (AUC)=0.81. If a CAP was classificated as bacterial, it was assessed with step 2. The step 2 differentiates typical vs. atypical bacterial CAP. Typical bacteria were classified with a sensitivity=100%, a specificity=64%, and AUC=0.90. Conclusion This two-steps tool can facilitate the physician decision to prescribe antibiotics without compromising patient safety.

scholarly journals Akurasi Diagnostik Prokalsitonin Sebagai Penanda Serologis Untuk Membedakan Antara Sepsis Bakterial dan Sepsis Virus

2017 ◽  
Vol 9 (3) ◽  
pp. 157
Author(s):  
Syafri Kamsul Arif ◽  
A. Muh. Farid Wahyuddin ◽  
A. Muh Takdir Musba
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Chi Square ◽  
Cross Sectional ◽  
Roc Area ◽  
Receiver Operating

Prokalsitonin merupakan penandadiagnostik yang baik pada sepsis khususnya sepsis yang disebabkan oleh bakteri. Penelitian ini bertujuan mengetahui sensitivitas, spesifisitas dan akurasi diagnostik prokalsitonin sebagai penandaserologis untuk membedakan antara sepsis bakterial dan virus pada pasien yang dirawat di Intensive Care Unit  (ICU) dan Infection Center (IC) RSUP Dr. Wahidin Sudirohusodo.Penelitian ini merupakan penelitian observasional analitik dengan desain cross sectional menggunakan data sekunder rekam medik pasien sepsis yang dirawat di ICU dan IC RSUP Dr. Wahidin Sudirohusodo, periode 1 Januari 2014 sampai dengan 31 Agustus 2016. Data tersebut dianalisa dengan uji T Independent,  Mann Whitney and Chi-Square dimana terdapat 80 sampel yang memenuhi kriteria inklusi.Dari penelitian ini, terdapat perbedaan yang signifikan dari kadarprokalsitonin antara sepsis bacterial(60.89 ± 73.651 ng/ml) dan sepsis virus (1.12 ± 0.622 ng/ml). Berdasarkan analisis Receiver Operating Characteristic (ROC), area Under the Curve (AUC) dari prokalsitonin adalah 0.841 dengan interval 0.758–0.925 dan signifikansi 95%. Kadar ambang diagnostik terbaik prokalsitonin yang didapatkan pada penelitian ini adalah 1.60 sebagai cut off point (sensitivitas: 82,4%, spesifisitas: 65,2% dan akurasi: 88,7%) untuk membedakan sepsis bakterial dengan sepsis virus. Prokalsitonin memiliki sensitivitas, spesifisitas dan akurasi diagnostik yang baik sebagai pembeda anatar seosis bakterial dan sepsis virus.

Vasculitis in the critically ill

2016 ◽  
Author(s):  
Karina A. Keogh
Keyword(s):  
Intensive Care ◽  
Blood Vessels ◽  
Severe Disease ◽  
Clinical Manifestations ◽  
Granulomatous Inflammation ◽  
Pulmonary Nodules ◽  
Organ Damage ◽  
Alveolar Haemorrhage ◽  
Intensive Care Treatment ◽  
Cytotoxic Therapies

The vasculitic syndromes are a heterogeneous group of rare disorders characterized by degrees of inflammation and necrosis of blood vessels with a wide variety of clinical manifestations. Intensive care treatment is most commonly required for vasculitis involving small blood vessels, including capillaries. Involvement of these vessels in the lung causes alveolar haemorrhage, which may lead to respiratory failure. In the kidneys it may cause glomerulonephritis leading to renal failure. Severe cardiac, neurological, and gastrointestinal manifestions can also be seen. Non-vasculitic manifestations may also be present, such as pulmonary nodules secondary to granulomatous inflammation in granulomatosis with polyangiitis. Diagnosis is based primarily on history and physical exam in conjunction with radiographic and serological testing. Intensive care unit admission is typically secondary to end organ damage due to inflammation, or because of side effects from the cytotoxic therapies, particularly infection. Treatment of vasculitis includes supportive management in conjunction with immunosuppression. Standard treatment of severe disease consists of corticosteroids and cytotoxic drugs.

scholarly journals A Retrospective Longitudinal Study of COVID-19 as Seen by a Large Urban Hospital in Chicago

2020 ◽  
Author(s):  
Haotian Chen ◽  
Yogatheesan Varatharajah ◽  
Sarah Stewart de Ramirez ◽  
Paul Arnold ◽  
Casey Frankenberger ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Intensive Care ◽  
Prognostic Model ◽  
Medical Center ◽  
Severe Disease ◽  
Ventilatory Support ◽  
Medical Resources ◽  
Clinical Variables ◽  
Risk Of Death ◽  
Increased Risk

AbstractThe rapid spread of the novel coronavirus disease 2019 (COVID-19) has created high demand for medical resources, including personnel, intensive care unit beds, and ventilators. As thousands of patients are hospitalized, the disease has shown remarkable diversity in its manifestation; many patients with mild to no symptoms recover from the disease requiring minimal care, but some patients with severe disease progression require mechanical ventilation support in intensive care units (ICU) with an increased risk of death. Studying the characteristics of patients in these various strata can help us understand the varied progression of this disease, enable earlier interventions for at-risk patients, and help manage medical resources more efficiently. This paper presents a retrospective analysis of 10,123 COVID-19 patients treated at the Rush University Medical Center in Chicago, including their demographics, symptoms, comorbidities, laboratory values, vital signs, and clinical history. Specifically, we present a staging scheme based on discrete clinical events (i.e., admission to the hospital, admission to the ICU, mechanical ventilation, and death), and investigate the temporal trend of clinical variables and the effect of comorbidities in each of those stages. We then developed a prognostic model to predict ventilation demands at an individual patient level by analyzing baseline clinical variables, which entails (1) a least absolute shrinkage and selection operator (LASSO) regression and a decision tree model to identify predictors for mechanical ventilation; and (2) a logistic regression model based on these risk factors to predict which patients will eventually need ventilatory support. Our results indicate that the prognostic model achieves an AUC of 0.823 (95% CI: 0.765–0.880) in identifying patients who will eventually require mechanical ventilation.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Saliva RNA biomarkers predict concussion duration and detect symptom recovery: a comparison with balance and cognitive testing

2021 ◽  
Author(s):  
Gregory Fedorchak ◽  
Aakanksha Rangnekar ◽  
Cayce Onks ◽  
Andrea C. Loeffert ◽  
Jayson Loeffert ◽  
...  
Keyword(s):  
Test Performance ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Cognitive Testing ◽  
Cognitive Test ◽  
Clinical Tool ◽  
Post Injury ◽  
Symptom Recovery ◽  
Cognitive Test Performance

Abstract Objective The goals of this study were to assess the ability of salivary non-coding RNA (ncRNA) levels to predict post-concussion symptoms lasting ≥ 21 days, and to examine the ability of ncRNAs to identify recovery compared to cognition and balance. Methods RNA sequencing was performed on 505 saliva samples obtained longitudinally from 112 individuals (8–24-years-old) with mild traumatic brain injury (mTBI). Initial samples were obtained ≤ 14 days post-injury, and follow-up samples were obtained ≥ 21 days post-injury. Computerized balance and cognitive test performance were assessed at initial and follow-up time-points. Machine learning was used to define: (1) a model employing initial ncRNA levels to predict persistent post-concussion symptoms (PPCS) ≥ 21 days post-injury; and (2) a model employing follow-up ncRNA levels to identify symptom recovery. Performance of the models was compared against a validated clinical prediction rule, and balance/cognitive test performance, respectively. Results An algorithm using age and 16 ncRNAs predicted PPCS with greater accuracy than the validated clinical tool and demonstrated additive combined utility (area under the curve (AUC) 0.86; 95% CI 0.84–0.88). Initial balance and cognitive test performance did not differ between PPCS and non-PPCS groups (p > 0.05). Follow-up balance and cognitive test performance identified symptom recovery with similar accuracy to a model using 11 ncRNAs and age. A combined model (ncRNAs, balance, cognition) most accurately identified recovery (AUC 0.86; 95% CI 0.83–0.89). Conclusions ncRNA biomarkers show promise for tracking recovery from mTBI, and for predicting who will have prolonged symptoms. They could provide accurate expectations for recovery, stratify need for intervention, and guide safe return-to-activities.

The presence of liver cirrhosis is a strong negative predictor of survival for patients admitted to the intensive care unit – Cirrhosis in intensive care patients

2021 ◽  
Author(s):  
Jörg Bojunga ◽  
Mireen Friedrich-Rust ◽  
Alica Kubesch ◽  
Kai Henrik Peiffer ◽  
Hannes Abramowski ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Liver Cirrhosis ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Treatment ◽  
Matched Cohort ◽  
Care Treatment ◽  
Cirrhotic Patients ◽  
The Impact ◽  
Cirrhotic Group

Abstract Background and Aims Liver cirrhosis is a systemic disease that substantially impacts the body’s physiology, especially in advanced stages. Accordingly, the outcome of patients with cirrhosis requiring intensive care treatment is poor. We aimed to analyze the impact of cirrhosis on mortality of intensive care unit (ICU) patients compared to other frequent chronic diseases and conditions. Methods In this retrospective study, patients admitted over three years to the ICU of the Department of Medicine of the University Hospital Frankfurt were included. Patients were matched for age, gender, pre-existing conditions, simplified acute physiology score (SAPS II), and therapeutic intervention scoring system (TISS). Results A total of 567 patients admitted to the ICU were included in the study; 99 (17.5 %) patients had liver cirrhosis. A total of 129 patients were included in the matched cohort for the sensitivity analysis. In-hospital mortality was higher in cirrhotic patients than non-cirrhotic patients (p < 0.0001) in the entire and matched cohort. Liver cirrhosis remained one of the strongest independent predictors of in-hospital mortality (entire cohort p = 0.001; matched cohort p = 0.03) along with dialysis and need for transfusion in the multivariate logistic regression analysis. Furthermore, in the cirrhotic group, the need for kidney replacement therapy (p < 0.001) and blood transfusion (p < 0.001) was significantly higher than in the non-cirrhotic group.  Conclusions In the presented study, liver cirrhosis was one of the strongest predictors of in-hospital mortality in patients needing intensive care treatment along with dialysis and the need for ventilation. Therefore, concerted efforts are needed to improve cirrhotic patients’ outcomes, prevent disease progression, and avoid complications with the need for ICU treatment in the early stages of the disease.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

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