Identification of care needs of patients with and without the use of a classification instrument

Objective: To analyze the agreement and disagreement between the assessments by applying or not a patient classification instrument, and to investigate the association between the agreement and personal and professional characteristics of the evaluators. Method: This is a descriptive exploratory study. 105 patients were hospitalized in a teaching hospital in the state of Sao Paulo, using the kappa statistic (weighted) and the Bootstrap method. Results: The agreement between the assessments were​​: kw 0.87 (instrument x internal evaluator), kw 0.78 (instrument x external evaluator) and kw 0.76 (between evaluators) and the influence of some personal and professional characteristics. The assessments conducted through the use of an instrument contemplated a greater number of areas of care in relation to when the instrument was not applied. Conclusion: The use of this instrument is recommended in order to more effectively identify care needs of patients.




Nursing care mapping for patients at risk of falls in the Nursing Interventions Classification

Objective: Identifying the prescribed nursing care for hospitalized patients at risk of falls and comparing them with the interventions of the Nursing Interventions Classifications (NIC). Method: A cross-sectional study carried out in a university hospital in southern Brazil. It was a retrospective data collection in the nursing records system. The sample consisted of 174 adult patients admitted to medical and surgical units with the Nursing Diagnosis of Risk for falls. The prescribed care were compared with the NIC interventions by the cross-mapping method. Results: The most prevalent care were the following: keeping the bed rails, guiding patients/family regarding the risks and prevention of falls, keeping the bell within reach of patients, and maintaining patients’ belongings nearby, mapped in the interventions Environmental Management: safety and Fall Prevention. Conclusion: The treatment prescribed in clinical practice was corroborated by the NIC reference.


Pediatric patient classification system: improvement of an instrument

Objective Improve the content validity of the instrument for classification of pediatric patients and evaluate its construct validity. Method A descriptive exploratory study in the measurement of the content validity index, and correlational design for construct validation through exploratory factor analysis. Results The content validity index for indicators was 0.99 and it was 0.97 for graded situations. Three domains were extracted in the construct validation, namely: patient, family and therapeutic procedures, with 74.97% of explained variance. The instrument showed evidences of content and construct validity. Conclusion The validation of the instrument occurred under the approach of family-centered care, and allowed incorporating some essential needs of childhood such as playing, interaction and affection in the content of the instrument.


La nouvelle classification de Savary des oesophagites de reflux [Savary's new endoscopic classification of reflux oesophagitis]

Comme la classification de Savary-Miller dont elle adopte l'essentiel, la classification de l'oesophagite par refluxen 5 types de Savary-Monnier repose sur une analyse rigoureuse et précise des lésions endoscopiques. Ses principales qualités sont d'être simple, complète, logique et souple. Son impact sur la pratique est certain puisqu'elle a une excellente valeur pronostique et qu'elle permet de choisir la bonne stratégie thérapeutique. De plus, en isolant les cicatrices cylindriques (seules précancéroses à surveiller à long terme) elle permet de les utiliser pour préciser la topographie des oesophagites de reflux.

Use of patients’ classification instruments: analysis of the brazilian production of knowledge

Objective To analyze the production of scientific knowledge about the use of patients’ classification instruments in care and management practice in Brazil. Method Integrative literature review with databases search in: Latin American and Caribbean Literature on Health Sciences (LILACS), Medical Literature Analysis and Retrieval System on-line (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and SCOPUS, between January 2002 through December 2013. Results 1,194 studies were found, 31 met the inclusion criteria. We observed a higher number of studies in the category care plans and workload (n=15), followed by the category evaluation of psychometric properties (n=14). Conclusion Brazilian knowledge production has not yet investigated some purposes of using instruments for classifying patients in professional nursing practice. The identification of unexplored areas can guide future research on the topic.


Terms of international classification for Nursing Practice in motor and physical rehabilitation

OBJECTIVE To validate terms of nursing language especially for physical-motor rehabilitation and map them to the terms of ICNP® 2.0. METHOD A methodology research based on document analysis, with collection and analysis of terms from 1,425 records. RESULTS 825 terms were obtained after the methodological procedure, of which 226 had still not been included in the ICNP® 2.0. These terms were distributed as follows: 47 on the Focus axis; 15 on the Judgment axis; 31 on the Action axis; 25 on the Location axis; 102 on the Means axis; three on the Time axis; and three on the Client axis. All non-constant terms in ICNP® have been validated by experts, having reached an agreement index ≥0.80. CONCLUSION The ICNP® is applicable and used in nursing care for physical-motor rehabilitation.

The InterPro protein families database: the classification resource after 15 years.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

Computerized advice on drug dosage to improve prescribing practice.

BACKGROUND: Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost effective fashion. This is an updated version of an earlier Cochrane systematic review, by Walton et al, published in 2001. OBJECTIVES: To assess whether computerised advice on drug dosage has beneficial effects on the process or outcome of health care. SEARCH STRATEGY: We searched the Cochrane Effective Practice and Organisation of Care Group specialized register (June 1996 to December 2006), MEDLINE (1966 to December 2006), EMBASE (1980 to December 2006), hand searched the journal Therapeutic Drug Monitoring (1979 to March 2007) and the Journal of the American Medical Informatics Association (1996 to March 2007) as well as reference lists from primary articles. SELECTION CRITERIA: Randomized controlled trials, controlled trials, controlled before and after studies and interrupted time series analyses of computerized advice on drug dosage were included. The participants were health professionals responsible for patient care. The outcomes were: any objectively measured change in the behaviour of the health care provider (such as changes in the dose of drug used); any change in the health of patients resulting from computerized advice (such as adverse reactions to drugs). DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and assessed study quality. MAIN RESULTS: Twenty-six comparisons (23 articles) were included (as compared to fifteen comparisons in the original review) including a wide range of drugs in inpatient and outpatient settings. Interventions usually targeted doctors although some studies attempted to influence prescriptions by pharmacists and nurses. Although all studies used reliable outcome measures, their quality was generally low. Computerized advice for drug dosage gave significant benefits by:1.increasing the initial dose (standardised mean difference 1.12, 95% CI 0.33 to 1.92)2.increasing serum concentrations (standradised mean difference 1.12, 95% CI 0.43 to 1.82)3.reducing the time to therapeutic stabilisation (standardised mean difference -0.55, 95%CI -1.03 to -0.08)4.reducing the risk of toxic drug level (rate ratio 0.45, 95% CI 0.30 to 0.70)5.reducing the length of hospital stay (standardised mean difference -0.35, 95% CI -0.52 to -0.17). AUTHORS' CONCLUSIONS: This review suggests that computerized advice for drug dosage has some benefits: it increased the initial dose of drug, increased serum drug concentrations and led to a more rapid therapeutic control. It also reduced the risk of toxic drug levels and the length of time spent in the hospital. However, it had no effect on adverse reactions. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimise the effect of computerised advice.

Laser therapy in pressure ulcers: evaluation by the Pressure Ulcer Scale for Healing and Nursing Outcomes Classification

AbstractOBJECTIVETo describe the pressure ulcer healing process in critically ill patients treated with conventional dressing therapy plus low-intensity laser therapy evaluated by the Pressure Ulcer Scale for Healing (PUSH) and the result of Wound Healing: Secondary Intention, according to the Nursing Outcomes Classification (NOC).METHODCase report study according to nursing process conducted with an Intensive Care Unit patient. Data were collected with an instrument containing the PUSH and the result of the NOC. In the analysis we used descriptive statistics, considering the scores obtained on the instrument.RESULTSA reduction in the size of lesions of 7cm to 1.5cm of length and 6cm to 1.1cm width, in addition to the increase of epithelial tissue and granulation, decreased secretion and odor.CONCLUSIONThere was improvement in the healing process of the lesion treated with adjuvant therapy and the use of NOC allowed a more detailed and accurate assessment than the PUSH.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.