Dataset Information

Imported severe malaria and risk factors for intensive care: A single-centre retrospective analysis.

ABSTRACT: OBJECTIVES:This study aims to identify the risk factors for intensive care (IC) in severe malaria patients admitted to the "Lazzaro Spallanzani" National Institute for Infectious Diseases, Rome, Italy. METHODS:All patients with confirmed severe malaria and hospitalized between 2007 and 2015 were included in the analysis and stratified into two groups: those requiring IC and those who did not. Five prognostic malaria scores were estimated; clinical severity at IC unit admission was assessed using the Sequential Organ Failure Assessment and the quick-Sequential Organ Failure Assessment scores. Univariate and multivariate analysis were performed to assess factors independently associated to IC. RESULTS:A total of 98 severe malaria patients were included; 10 of them required IC. There were no deaths or sequelae. Patients requiring IC had higher severity scores. At the multivariate analysis, only the number of World Health Organization criteria and the aspartate aminotransferase value were independently associated with the need of IC. CONCLUSIONS:An early and accurate assessment of the severity score is essential for the management of severe malaria patients.

SUBMITTER: D'Abramo A

PROVIDER: S-EPMC6855647 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Imported severe malaria and risk factors for intensive care: A single-centre retrospective analysis.

D'Abramo Alessandra A Lepore Luciana L Iannetta Marco M Gebremeskel Tekle Saba S Corpolongo Angela A Scorzolini Laura L Bevilacqua Nazario N Mariano Andrea A Giancola Maria Letizia ML Vulcano Antonella A Maritti Micaela M Agresta Alessandro A Antonini Mario M D'Alessandro Umberto U Nicastri Emanuele E

PloS one 20191114 11

<h4>Objectives</h4>This study aims to identify the risk factors for intensive care (IC) in severe malaria patients admitted to the "Lazzaro Spallanzani" National Institute for Infectious Diseases, Rome, Italy.<h4>Methods</h4>All patients with confirmed severe malaria and hospitalized between 2007 and 2015 were included in the analysis and stratified into two groups: those requiring IC and those who did not. Five prognostic malaria scores were estimated; clinical severity at IC unit admission was ...[more]

PMID: 31725774

Similar Datasets

Project description:Malaria is a leading cause of imported febrile illnesses in pediatric travelers, but few studies have addressed severe imported pediatric malaria. We aimed to determine the risk factors and the features of imported pediatric severe malaria.We conducted a retrospective, descriptive study using the French National Reference Center for Imported Malaria database, in children aged 0-15 years who were hospitalized with a falciparum malaria from January 1st 1996 to December 31th 2005. Uncomplicated and severe cases of falciparum malaria were compared to identify risk factors for severe cases. In the hospitals that reported more than five severe cases during the study period, we evaluated severe cases for prognostic factors and assessed the accuracy WHO criteria for predicting severity. Given the rarity of deaths, adverse outcomes were defined as requiring major therapeutic procedures (MTPs)-e.g., sedation, mechanical ventilation, nasal oxygen therapy, blood transfusions, hemodialysis, fluid resuscitation-or pediatric intensive care unit (PICU) admission.Of 4150 pediatric malaria cases included in the study, 3299 were uncomplicated and 851 (20.5%) were severe. Only one death was recorded during this period. Predictors for severe falciparum malaria were: age <2 years (OR = 3.2, 95% CI = 2.5-4.0, p <0.0001) and a travel in the Sahelian region (OR = 1.7, 95% CI = 1.3-2.0, p = 0.0001). Of 422 severe malaria cases, a stay in a Sahelian region, lack of chemoprophylaxis, age <2 years or thrombocytopenia <100 x 10^3/mm^3 predicted adverse outcomes. Except for the hyperparasitemia threshold of 4%, the main WHO 2000 criteria for severe malaria reliably predicted adverse outcomes. In our study, the threshold of parasitemia most predictive of a poor outcome was 8%.In imported pediatric malaria, children younger than 2 years deserve particular attention. The main WHO 2000 criteria for severity are accurate, except for the threshold of hyperparasitemia, which should be revised.

Project description:BackgroundInvasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited.MethodA set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F1 score, accuracy, and AUC.ResultsOf the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%.ConclusionsIn this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients' early intervention.

Dataset Information

Imported severe malaria and risk factors for intensive care: A single-centre retrospective analysis.

Publications

Imported severe malaria and risk factors for intensive care: A single-centre retrospective analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets