Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza.
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ABSTRACT:
SUBMITTER: Lin S
PROVIDER: S-EPMC8764189 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:INTRODUCTION:The available prognostic scoring systems for severe acute pancreatitis (SAP) have limitations that restrict their clinical value. The aim of this study was to develop a simple model (score) that could rapidly identify those at risk for SAP. METHODS:We derived a risk model using a retrospective cohort of 700 patients by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The classification and regression tree (CART) analysis was used to create risk categories. The model was internally validated by a tenfold cross-validation and externally validated in a separate prospective cohort of 194 patients. RESULTS:The incidence of SAP was 9.7% in the derivation cohort and 9.3% in the validation cohort. A prognostic score (We denoted it as the SABP score), ranging from 0 to 10, consisting of systemic inflammatory response syndrome, serum albumin, blood urea nitrogen and pleural effusion, was developed by logistic regression and bootstrapping analysis. Patients could be divided into three risk categories according to total SABP score based on CART analysis. The mean probability of developing SAP was 1.9%, 12.8% and 41.6% in patients with low (0-3), moderate (4-6) and high (7-10) SABP score, respectively. The AUCs of prognostic score in tenfold cross-validation was 0.873 and 0.872 in the external validation. CONCLUSION:Our risk prediction score may assist physicians in predicting the development of SAP.
| S-EPMC6505180 | biostudies-other
Project description:We aimed to develop and validate a risk score to aid in the early identification of laboratory-confirmed dengue patients at high risk of severe dengue (SD) (i.e. severe plasma leakage with shock or respiratory distress, or severe bleeding or organ impairment). We retrospectively analyzed data of 1184 non-SD patients at hospital presentation and 69 SD patients before SD onset. We fit a logistic regression model using 85% of the population and converted the model coefficients to a numeric risk score. Subsequently, we validated the score using the remaining 15% of patients. Using the derivation cohort, two scoring algorithms for predicting SD were developed: models 1 (dengue illness ≤4 days) and 2 (dengue illness >4 days). In model 1, we identified four variables: age ≥65 years, minor gastrointestinal bleeding, leukocytosis, and platelet count ≥100×10(9) cells/L. Model 1 (ranging from -2 to +6 points) showed good discrimination between SD and non-SD, with an area under the receiver operating characteristic curve (AUC) of 0.848 (95% confidence interval [CI], 0.771-0.924). The optimal cutoff value for model 1 was 1 point, with a sensitivity and specificity for predicting SD of 70.3% and 90.6%, respectively. In model 2 (ranging from 0 to +3 points), significant predictors were age ≥65 years and leukocytosis. Model 2 showed an AUC of 0.859 (95% CI, 0.756-0.963), with an optimal cutoff value of 1 point (sensitivity, 80.3%; specificity, 85.8%). The median interval from hospital presentation to SD was 1 day. This finding underscores the importance of close monitoring, timely resuscitation of shock including intravenous fluid adjustment and early correction of dengue-related complications to prevent the progressive dengue severity. In the validation data, AUCs of 0.904 (95% CI, 0.825-0.983) and 0.917 (95% CI, 0.833-1.0) in models 1 and 2, respectively, were achieved. The observed SD rates (in both cohorts) were <3% for patients with a score <1 point, but >50% for those with a score of ≥2 points, irrespective of the day of illness onset, suggesting that our simple risk score can be easily implemented in resource-limited countries for early prediction of dengue patients at risk of SD provided that they have rapid dengue confirmed tests. For patients with other acute febrile illnesses or bacterial infections usually have SD risk score of >1. Thus, these scoring algorithms cannot totally replace good clinical judgement of the physician, and most importantly, early differentiating dengue from other febrile illnesses is critical for appropriate monitoring and management.
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Project description:Administrative data are used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score.Retrospective cohort study using 2012 administrative data from five U.S. states. Three cohorts of patients with severe sepsis were created: 1) International Classification of Diseases, 9th Revision, Clinical Modification codes for severe sepsis/septic shock, 2) Martin approach, and 3) Angus approach. The model was developed and internally validated in International Classification of Diseases, 9th Revision, Clinical Modification, cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score.Acute care, nonfederal hospitals in New York, Maryland, Florida, Michigan, and Washington.Patients in one of three severe sepsis cohorts: 1) explicitly coded (n = 108,448), 2) Martin cohort (n = 139,094), and 3) Angus cohort (n = 523,637) INTERVENTIONS: None.Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit and C-statistics, respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. Goodness-of-fit demonstrated p value of more than 0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort), suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile.Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data.
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Project description:Data on risk factors for severe outcomes from 2009 pandemic influenza A (H1N1) virus infection are limited outside of developed countries.We reviewed medical charts to collect data from patients hospitalized with laboratory-confirmed 2009 H1N1 infection who were identified across China during the period from September 2009 through February 2010, and we analyzed potential risk factors associated with severe illness (defined as illness requiring intensive care unit admission or resulting in death).Among 9966 case patients, the prevalence of chronic medical conditions (33% vs 14%), pregnancy (15% vs 7%), or obesity (19% vs 14%) was significantly higher in those patients with severe illness than it was in those with less severe disease. In multivariable analyses, among nonpregnant case patients aged ? 2 years, having a chronic medical condition significantly increased the risk of severe outcome among all age groups, and obesity was a risk factor among those <60 years of age. The risk of severe illness among pregnant case patients was significantly higher for those in the second and third trimesters. The risk of severe illness was increased when oseltamivir treatment was initiated ? 5 days after illness onset (odds ratio, 1.42; 95% confidence interval, 1.20-1.67). For persons <60 years of age, the prevalence of obesity among case patients with severe illness was significantly greater than it was among those without severe illness or among the general population.Risk factors for severe 2009 H1N1 illness in China were similar to those observed in developed countries, but there was a lower prevalence of chronic medical conditions and a lower prevalence of obesity. Obesity was a risk factor among case patients < 60 years of age. Early initiation of oseltamivir treatment was most beneficial, and there was an increased risk of severe disease when treatment was started ? 5 days after illness onset.
| S-EPMC3060897 | biostudies-other
Project description:This case-control study was carried out to estimate risk factors associated with hospitalizations and severe outcomes [intensive care unit (ICU) admission or death] among patients with illness because of laboratory-confirmed 2009 pandemic A/H1N1 virus (pH1N1) during the first wave of pH1N1 activity in the province of Quebec, Canada.We collected epidemiologic information by phone using a standardized questionnaire from patients with laboratory-confirmed pH1N1 illness during the first spring/summer pandemic wave in Quebec, Canada. Risk factors associated with hospitalization were assessed by comparing hospitalized to community cases and for ICU admission or death through comparison with hospitalized cases.Cases (321 hospitalized patients including 47 ICU admissions and 15 deaths) were compared to controls (395 non-hospitalized patients) by using multivariable logistic regression adjusted for gender, age, education, being a health care worker, smoking, seasonal influenza vaccination, delay to consultation, antiviral use before admission, pregnancy, underlying medical conditions, and obesity. Age <5 years, underlying medical conditions (neuromuscular, cardiac, pulmonary, and renal conditions, diabetes, asthma, and other), and delayed consultation were associated with hospitalization. The strongest association with hospitalization was observed for neuromuscular disorders. Antiviral medication before hospital admission protected against severe disease. Association of obesity with hospitalization was not significant after adjustment in multivariable analysis. Among hospitalized patients, age ≥60 years and immune suppression were associated with death.Previously identified risk factors for seasonal influenza were also associated with increased risk of severe pH1N1 outcomes. The independent role of obesity needs to be further defined.
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Project description:BackgroundSince the start of the 2009 influenza A pandemic (H1N1pdm), the World Health Organization and its member states have gathered information to characterize the clinical severity of H1N1pdm infection and to assist policy makers to determine risk groups for targeted control measures.Methods and findingsData were collected on approximately 70,000 laboratory-confirmed hospitalized H1N1pdm patients, 9,700 patients admitted to intensive care units (ICUs), and 2,500 deaths reported between 1 April 2009 and 1 January 2010 from 19 countries or administrative regions--Argentina, Australia, Canada, Chile, China, France, Germany, Hong Kong SAR, Japan, Madagascar, Mexico, The Netherlands, New Zealand, Singapore, South Africa, Spain, Thailand, the United States, and the United Kingdom--to characterize and compare the distribution of risk factors among H1N1pdm patients at three levels of severity: hospitalizations, ICU admissions, and deaths. The median age of patients increased with severity of disease. The highest per capita risk of hospitalization was among patients <5 y and 5-14 y (relative risk [RR]?=?3.3 and 3.2, respectively, compared to the general population), whereas the highest risk of death per capita was in the age groups 50-64 y and ?65 y (RR?=?1.5 and 1.6, respectively, compared to the general population). Similarly, the ratio of H1N1pdm deaths to hospitalizations increased with age and was the highest in the ?65-y-old age group, indicating that while infection rates have been observed to be very low in the oldest age group, risk of death in those over the age of 64 y who became infected was higher than in younger groups. The proportion of H1N1pdm patients with one or more reported chronic conditions increased with severity (median?=?31.1%, 52.3%, and 61.8% of hospitalized, ICU-admitted, and fatal H1N1pdm cases, respectively). With the exception of the risk factors asthma, pregnancy, and obesity, the proportion of patients with each risk factor increased with severity level. For all levels of severity, pregnant women in their third trimester consistently accounted for the majority of the total of pregnant women. Our findings suggest that morbid obesity might be a risk factor for ICU admission and fatal outcome (RR?=?36.3).ConclusionsOur results demonstrate that risk factors for severe H1N1pdm infection are similar to those for seasonal influenza, with some notable differences, such as younger age groups and obesity, and reinforce the need to identify and protect groups at highest risk of severe outcomes. Please see later in the article for the Editors' Summary.
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Project description:Pandemic influenza viruses often cause severe disease in middle-aged adults without preexisting comorbidities. The mechanism of illness associated with severe disease in this age group is not well understood. Here we find preexisting serum antibodies that cross-react with, but do not protect against, 2009 H1N1 influenza virus in middle-aged adults. Nonprotective antibody is associated with immune complex-mediated disease after infection. We detected high titers of serum antibody of low avidity for H1-2009 antigen, and low-avidity pulmonary immune complexes against the same protein, in severely ill individuals. Moreover, C4d deposition--a marker of complement activation mediated by immune complexes--was present in lung sections of fatal cases. Archived lung sections from middle-aged adults with confirmed fatal influenza 1957 H2N2 infection revealed a similar mechanism of illness. These observations provide a previously unknown biological mechanism for the unusual age distribution of severe cases during influenza pandemics.
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Project description:Osteosarcoma is the most common primary solid malignancy of the bone, and its high mortality usually correlates with early metastasis. In this study, we developed a risk score model to help predict metastasis at the time of diagnosis. We downloaded and mined four expression profile datasets associated with osteosarcoma metastasis from the Gene Expression Omnibus. After data normalization, we performed LASSO logistic regression analysis together with 10-fold cross validation using the GSE21257 dataset. A combination of eight genes (RAB1,CLEC3B,FCGBP,RNASE3,MDL1,ALOX5AP,VMO1 and ALPK3) were identified as being associated with osteosarcoma metastasis. These genes were put into a gene risk score model, and the prediction efficiency of the model was then validated using three independent datasets (GSE33383, GSE66673, and GSE49003) by plotting receiver operating characteristic curves. The expression levels of the eight genes in all datasets were shown as heatmaps, and gene ontology gene annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. These eight genes play a role in cancer-related biological processes, such as apoptosis and biosynthetic processes. Our results may aid in elucidating the possible mechanisms of osteosarcoma metastasis, and may help to facilitate the individual management of patients with osteosarcoma after treatment.
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