Heart-lung interactions with different ventilatory settings during acute lung injury and hypovolaemia: an experimental study

Background. The functional haemodynamic variables pulse pressure variation (PPV), stroke volume variation (SVV), and systolic pressure variation (SPV) are widely used to assess haemodynamic status. However, it is not known how these perform during acute lung injury (ALI). This study evaluated the effects of different ventilatory strategies on haemodynamic parameters in pigs with ALI during normovolaemia and hypovolaemia. Methods. Eight anaesthetized Agroceres pigs [40 (1.9) kg] were instrumented with pulmonary artery, PiCCO, and arterial catheters and ventilated. Three ventilatory settings were randomly assigned for 10 min each: tidal volume (VT) 15 ml kg(-1) and PEEP 5 cm H(2)O, VT 8 ml kg(-1) and PEEP 13 cm H(2)O, or VT 6 ml kg(-1) and PEEP 13 cm H(2)O. Data were collected at each setting at baseline, after ALI (lung lavage+Tween 1.5%), and ALI with hypovolaemia (haemorrhage to 30% of estimated blood volume). Results. At baseline, high VT increased PPV, SVV, and SPV (P < 0.05 for all). During ALI, high VT significantly increased PPV and SVV [(P = 0.002 and P = 0.008) respectively.]. After ALI with hypovolaemia, ventilation at VT 6 ml kg(-1) and PEEP 13 cm H(2)O decreased the accuracy of functional haemodynamic variables to predict hypovolaemia, with the exception of PPV (area under the curve 0.875). The parameters obtained by PiCCO were less influenced by ventilatory changes. Conclusions. VT is the ventilatory parameter which influences functional haemodynamics the most. During ventilation with low VT and high PEEP, most functional variables are less able to accurately predict hypovolaemia secondary to haemorrhage, with the exception of PPV.

Screening for Alzheimer`s Disease Among Illiterate Elderly: Accuracy Analysis for Multiple Instruments

One of the challenges in screening for dementia in developing countries is related to performance differences due to educational and cultural factors. This study evaluated the accuracy of single screening tests as well as combined protocols including the Mini-Mental State Examination (MMSE), Verbal Fluency animal category (VF), Clock Drawing test (CDT), and Pfeffer Functional Activities Questionnaire (PFAQ) to discriminate illiterate elderly with and without Alzheimer`s disease (AD) in a clinical sample. Cross-sectional study with 66 illiterate outpatients diagnosed with mild and moderate AD and 40 illiterate normal controls. Diagnosis of AD was based on NINCDS-ADRDA. All patients were submitted to a diagnostic protocol including a clinical interview based on the CAMDEX sections. ROC curves area analyses were carried out to compare sensitivity and specificity for the cognitive tests to differentiate the two groups (each test separately and in two by two combinations). Scores for all cognitive (MMSE, CDT, VF) and functional assessments (PFAQ) were significantly different between the two groups (p < 0.001). The best screening instruments for this sample of illiterate elderly were the MMSE and the PFAQ. The cut-off scores for the MMSE, VF, CDT, and PFAQ were 17.5, 7.5, 2.5, and 11.5, respectively. The most sensitive combination came from the MMSE and PFAQ (94.1%), and the best specificity was observed with the combination of the MMSE and CDT (89%). Illiterate patients can be successfully screened for AD using well-known screening instruments, especially in combined protocols.

Twelve-Month Prevalence of and Risk Factors for Suicide Attempts in the World Health Organization World Mental Health Surveys

Objective: Although suicide is a leading cause of death worldwide, clinicians and researchers lack a data-driven method to assess the risk of suicide attempts. This study reports the results of an analysis of a large cross-national epidemiologic survey database that estimates the 12-month prevalence of suicidal behaviors, identifies risk factors for suicide attempts, and combines these factors to create a risk index for 12-month suicide attempts separately for developed and developing countries. Method: Data come from the World Health Organization (WHO) World Mental Health (WMH) Surveys (conducted 2001-2007), in which 108,705 adults from 21 countries were interviewed using the WHO Composite International Diagnostic Interview. The survey assessed suicidal behaviors and potential risk factors across multiple domains, including socio-demographic characteristics, parent psychopathology, childhood adversities, DSM-IV disorders, and history of suicidal behavior. Results: Twelve-month prevalence estimates of suicide ideation, plans, and attempts are 2.0%, 0.6%, and 0.3%, respectively, for developed countries and 2.1%, 0.7%, and 0.4%, respectively, for developing countries. Risk factors for suicidal behaviors in both developed and developing countries include female sex, younger age, lower education and income, unmarried status, unemployment, parent psychopathology, childhood adversities, and presence of diverse 12-month DSM-IV mental disorders. Combining risk factors from multiple domains produced risk indices that accurately predicted 12-month suicide attempts in both developed and developing countries (area under the receiver operating characteristic curve = 0.74-0.80). Conclusions: Suicidal behaviors occur at similar rates in both developed and developing countries. Risk indices assessing multiple domains can predict suicide attempts with fairly good accuracy and may be useful in aiding clinicians in the prediction of these behaviors. J Clin Psychiatry 2010;71(12):1617-1628 (C) Copyright 2010 Physicians Postgraduate Press, Inc.

Consistency of FDG-PET Accuracy and Cost-Effectiveness in Initial Staging of Patients With Hodgkin Lymphoma Across Jurisdictions

Introduction: Two hundred ten patients with newly diagnosed Hodgkin`s lymphoma (HL) were consecutively enrolled in this prospective trial to evaluate the cost-effectiveness of fluorine-18 ((18)F)-fluoro-2-deoxy-D-glucose-positron emission tomography (FDG-PET) scan in initial staging of patients with HL. Methods: All 210 patients were staged with conventional clinical staging (CCS) methods, including computed tomography (CT), bone marrow biopsy (BMB), and laboratory tests. Patients were also submitted to metabolic staging (MS) with whole-body FDG-PET scan before the beginning of treatment. A standard of reference for staging was determined with all staging procedures, histologic examination, and follow-up examinations. The accuracy of the CCS was compared with the MS. Local unit costs of procedures and tests were evaluated. Incremental cost-effectiveness ratio (ICER) was calculated for both strategies. Results: In the 210 patients with HL, the sensitivity for initial staging of FDG-PET was higher than that of CT and BMB in initial staging (97.9% vs. 87.3%; P < .001 and 94.2% vs. 71.4%, P < 0.003, respectively). The incorporation of FDG-PET in the staging procedure upstaged disease in 50 (24%) patients and downstaged disease in 17 (8%) patients. Changes in treatment would be seen in 32 (15%) patients. Cumulative cost for staging procedures was $3751/patient for CCS compared to $5081 for CCS + PET and $4588 for PET/CT. The ICER of PET/CT strategy was $16,215 per patient with modified treatment. PET/CT costs at the beginning and end of treatment would increase total costs of HL staging and first-line treatment by only 2%. Conclusion: FDG-PET is more accurate than CT and BMB in HL staging. Given observed probabilities, FDG-PET is highly cost-effective in the public health care program in Brazil.

Patient Characteristics as Predictors of Image Quality and Diagnostic Accuracy of MDCT Compared With Conventional Coronary Angiography for Detecting Coronary Artery Stenoses: CORE-64 Multicenter International Trial

OBJECTIVE. The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis. MATERIALS AND METHODS. Two hundred ninety-one patients with a coronary artery calcification (CAC) score of <= 600 Agatston units (214 men and 77 women; mean age, 59.3 +/- 10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (>= 50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves. RESULTS. Increasing body mass index (BMI) (odds ratio [OR] = 0.89, p < 0.001), increasing heart rate (OR = 0.90, p < 0.001), and the presence of breathing artifact (OR = 4.97, p = 0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR = 0.58, p = 0.04). At a vessel level, CAC score (10 Agatston units) (OR = 1.03, p = 0.012) and patient age (OR = 1.02, p = 0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p = 0.08). CONCLUSION. Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

Behavioral disturbances in Noonan Syndrome: Brazilian preliminary data

Different from other countries Of Europe and North American, studies about the behavioral profile of Noonan syndrome`s patients are inexistent. The objective of this study was to report the profiles of behavioral functions of 10 participants (4 females and 6 males), with mutations in the PTPN11 gene. For this assessment it was used the Inventory of Behaviors of Children and Adolescents from 6 to 18 years (CBCL/6-18) and the Inventory of Auto-Evaluation for Adults from 18 to 59 years (ASR). The main results point that in Adaptive Functioning Scale all the participants were in the normality range. In the Syndrome Scale the adult participants were in normality range and the children were in clinical range to the sub-scales anxious/depressed, somatic complaints and aggressive behavior. In the DSM-Oriented Scale, 25% of the adult patients were in the borderline clinical range and clinical range, respectively, for Avoidant Personality Problems and Antisocial Personality Problems. About the both children in this scale were in the clinical range of Affective Problems and Anxiety Problems. This relatively homogenous sample, regarding the PTPN11 gene, shows a normal adult behavioral profile, on the average. However, the individual children anti adult profiles show diverse internalizing and externalizing behavioral disturbances.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Clinico-pathological discrepancies in the diagnoses of solid malignancies

Autopsy is a valuable tool in evaluating diagnostic accuracy. Solid malignancies may have a protracted presentation, and diagnosis frequently requires imaging and deep-sited biopsies; clinical and postmortem diagnosis discrepancies may occur in a high rate in these diseases. Here, we analyzed the occurrence of clinico-pathological discrepancies in the diagnoses of solid malignancies in a Brazilian academic hospital. We reviewed charts and autopsy reports of the patients that died from 2001 to 2003 with at least one solid neoplasm. Patients were classified in concordant and discordant cases regarding cancer diagnosis. Discordant cases were categorized in undiagnosed cases (no suspicion of cancer) and in misdiagnosed cases (clinical suspicion of cancer but incompletely diagnosed). Among the 264 patients with a single non-incidental solid neoplasm, the clinico-pathological discrepancy rate was 37.1%. Liver (22.5%), lung (19.4%), and pancreatic cancer (15.3%) were the most frequent malignancies in the discordant group. Misdiagnosis category comprised 68% of the discordant cases, i.e., there was no correct knowledge about the tumor primary site and/or the histological type during life. Our data show that a high rate of discrepancies occurs in solid malignancies. Autopsies may provide the basis for a better understanding of diagnostic deficiencies in different circumstances. (C) 2008 Elsevier GmbH. All rights reserved.

Analysis of Detergent-Insoluble and Whole Cell Lysate Fractions of Resting Neutrophils Using High-Resolution Mass Spectrometry

Neutrophilic granulocytes play a major role in the initiation and resolution of the inflammatory response, and demonstrate significant transcriptional and translational activity. Although much was known about neutrophils prior to the introduction of proteomics, the use of MS-based methodologies has provided an unprecedented tool to confirm and extend previous findings. In the present study, we performed a Gel-LC-MS/MS analysis of neutrophil detergent insoluble and whole cell lysate fractions of resting neutrophils. We achieved a set of identifications through the use of high-resolution mass spectrometry and validation of its data. We identified a total of 1249 proteins with a wide range of intensities from both detergent-insoluble and whole cell lysate fractions, allowing a mapping of proteins such as those involved in intracellular transport (Rab and Sec family proteins) and cell signaling (S100 proteins). These results represent the most comprehensive proteomic characterization of resting human neutrophils to date, and provide important information relevant for further studies of the immune system in health and disease. The methods applied here can be employed to help us understand how neutrophils respond to various physiologic and pathophysiologic conditions and could be extended to protein quantitation after cell activation.

Assessing Interethnic Admixture Using an X-Linked Insertion-Deletion Multiplex

In this study, a PCR multiplex was optimized, allowing the simultaneous analysis of 13 X-chromosome Insertion/deletion polymorphisms (INDELs). Genetic variation observed in Africans, Europeans, and Native Americans reveals high inter-population variability. The estimated proportions of X-chromosomes in an admixed population from the Brazilian Amazon region show a predominant Amerindian contribution (congruent to 41%), followed by European (congruent to 32%) and African (congruent to 27%) contributions. The proportion of Amerindian contribution based on X-linked data is similar to the expected value based on mtDNA and Y-chromosome information. The accuracy for assessing interethnic admixture, and the high differentiation between African, European, and Native American populations, demonstrates the suitability of this INDEL set to measure ancestry proportions in three-hybrid populations, as it is the case of Latin American populations. Am. J. Hum. Biol. 21:707-709, 2009. (C) 2009 Wiley-Liss, Inc.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.