A randomized controlled trial to assess the impact of psychoeducation on the quality of life of parents with children with congenital heart defects-Quantitative component

PURPOSE

To develop, implement and assess the results of psychoeducation to improve the QoL of parents with CHD newborns.

METHODS

Participants were parents of inpatient newborns with the diagnosis of non-syndromic CHD. We conducted a parallel RCT with an allocation ratio of 1:1 (intervention vs. control), considering the newborns, using mixed methods research. The intervention group received psychoeducation (Parental Psychoeducation in CHD [PPeCHD]) and the usual routines, and the control group received just the regular practices. The allocation concealment was assured. PI was involved in enrolling participants, developing and implementing the intervention, data collection and data analysis. We followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.

RESULTS

Parents of eight newborns were allocated to the intervention group (n = 15 parents) and eight to the control group (n = 13 parents). It was performed as an intention-to-treat (ITT) analysis. In M2 (4 weeks), the intervention group presented better QoL levels in the physical, psychological, and environmental domains of World Health Organization Quality of Life instrument (WHOQOL-Bref). In M3 (16 weeks), scores in physical and psychological domains maintained a statistically significant difference between the groups.

CONCLUSIONS

The PPeCHD, the psychoeducational intervention we developed, positively impacted parental QoL. These results support the initial hypothesis. This study is a fundamental milestone in this research field, adding new essential information to the literature.

Improving the quality of life of parents of patients with congenital abnormalities using psychoeducational interventions: a systematic review

PURPOSE

To identify psychoeducational interventions that target parents of children with congenital abnormalities (CA) and evaluate their impact on quality of life (QoL).

METHODS

The search was conducted in six electronic databases, complemented by references of the studies found, studies of evidence synthesis, a manual search of relevant scientific meetings' abstracts and contact with experts. We included primary studies on parents of children with CA that studied psychoeducational interventions versus standard care. We assessed the risk of bias using Cochrane Collaboration's tool.

RESULTS

We included six studies focusing on congenital heart defects (CHD). They described four different psychoeducational strategies. In four studies, statistically significant differences were found. For clinical practice, we considered three interventions as more feasible: the Educational program for mothers, with a group format of four sessions weekly; CHIP-Family intervention, which includes a parental group workshop followed by an individual follow-up booster session; and WeChat educational health program with an online format.

CONCLUSIONS

This review is the first that assesses the impact of psychoeducational interventions targeted at parents of children with CA on their QoL. The best approach to intervention is multiple group sessions. Two essential strategies were to give support material, enabling parents to review, and the possibility of an online program application, increasing accessibility. However, because all included studies focus on CHD, generalizations should be made carefully. These findings are crucial to guide future research to promote and improve comprehensive and structured support for families and integrate them into daily practice.

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity

Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

Dynamic treatment regimes (DTRs) to study treatment sequencing in oncology: a scoping review protocol

INTRODUCTION

The rapid evolution of the therapeutic landscape in oncology poses challenges to optimal treatment sequencing. Evidence for clinical decision-making is often limited to studies focused on treatment evaluation at a single decision point, with limited capability of identifying delayed effects of prior treatment decisions on the efficacy and feasibility of future treatments. There is a growing interest in dynamic treatment regimes (DTRs) evaluation as it provides guidance on treatment individualisation based on evolving treatment and patient characteristics. In this scoping review we aim to systematically map how and to what extent DTRs have been evaluated in clinical studies to generate evidence for clinical decision-making in oncology.

METHODS AND ANALYSIS

We will do a systematic literature search in MEDLINE (PubMed), Web of Science, Scopus and WHO international clinical trials registry platform to identify clinical studies (including protocols of ongoing studies), with either experimental or observational design, that aim to answer a clinical question and explore treatment sequencing issues in oncology using the concept of DTR. Data extraction will comprise information concerning cancer disease, clinical setting, treatments, tailoring variables, decision rules, decision points and outcomes, type of data, study design and statistical methods used for DTR evaluation. The review will be conducted according to Joanna Briggs Institute Reviewer's manual for scoping reviews. No patients will be involved.

ETHICS AND DISSEMINATION

Ethics committee approval is not required as this scoping review will undertake secondary analysis of published literature. Results will be disseminated through a peer-reviewed scientific journal and presented in relevant conferences. This scoping review will provide a better understanding of the methods used to generate evidence on treatment sequencing in oncology and will contribute to the identification of knowledge and methodological gaps that should be addressed.

Correction: Effectiveness of Secondary Risk-Reducing Strategies in Patients With Unilateral Breast Cancer With Pathogenic Variants of BRCA1 and BRCA2 Subjected to Breast-Conserving Surgery: Evidence-Based Simulation Study

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Impact of multimorbidity patterns in hospital admissions: the case study of asthma

Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. We assessed the dataset containing a registration of all Portuguese hospitalisations between 2011-2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants' age group. We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3-3.2 additional days), in-hospital mortality (OR range = 1.4-7.9) and hospital charges (average additional charges of 351.0 to 1025.8 Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.

The Association Between Comorbidities and Prescribed Drugs in Patients With Suspected Obstructive Sleep Apnea: Inductive Rule Learning Approach

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Effectiveness of Secondary Risk-Reducing Strategies in Patients With Unilateral Breast Cancer With Pathogenic Variants of BRCA1 and BRCA2 Subjected to Breast-Conserving Surgery: Evidence-Based Simulation Study

BACKGROUND

Approximately 62% of patients with breast cancer with a pathogenic variant (BRCA1 or BRCA2) undergo primary breast-conserving therapy.

OBJECTIVE

The study aims to develop a personalized risk management decision support tool for carriers of a pathogenic variant (BRCA1 or BRCA2) who underwent breast-conserving therapy for unilateral early-stage breast cancer.

METHODS

We developed a Bayesian network model of a hypothetical cohort of carriers of BRCA1 or BRCA2 diagnosed with stage I/II unilateral breast cancer and treated with breast-conserving treatment who underwent subsequent second primary cancer risk-reducing strategies. Using event dependencies structured according to expert knowledge and conditional probabilities obtained from published evidence, we predicted the 40-year overall survival rate of different risk-reducing strategies for 144 cohorts of women defined by the type of pathogenic variants (BRCA1 or BRCA2), age at primary breast cancer diagnosis, breast cancer subtype, stage of primary breast cancer, and presence or absence of adjuvant chemotherapy.

RESULTS

Absence of adjuvant chemotherapy was the most powerful factor that was linked to a dramatic decline in survival. There was a negligible decline in the mortality in patients with triple-negative breast cancer, who received no chemotherapy and underwent any secondary risk-reducing strategy, compared with surveillance. The potential survival benefit from any risk-reducing strategy was more modest in patients with triple-negative breast cancer who received chemotherapy compared with patients with luminal breast cancer. However, most patients with triple-negative breast cancer in stage I benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy or just risk-reducing salpingo-oophorectomy. Most patients with luminal stage I/II unilateral breast cancer benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy. The impact of risk-reducing salpingo-oophorectomy in patients with luminal breast cancer in stage I/II increased with age. Most older patients with the BRCA1 and BRCA2 pathogenic variants in exons 12-24/25 with luminal breast cancer may gain a similar survival benefit from other risk-reducing strategies or surveillance.

CONCLUSIONS

Our study showed that it is mandatory to consider the complex interplay between the types of BRCA1 and BRCA2 pathogenic variants, age at primary breast cancer diagnosis, breast cancer subtype and stage, and received systemic treatment. As no prospective study results are available at the moment, our simulation model, which will integrate a decision support system in the near future, could facilitate the conversation between the health care provider and patient and help to weigh all the options for risk-reducing strategies leading to a more balanced decision.

Dataset Comparison Tool: Utility and Privacy

Synthetic data has been more and more used in the last few years. While its applications are various, measuring its utility and privacy is seldom an easy task. Since there are different methods of evaluating these issues, which are dependent on data types, use cases and purpose, a generic method for evaluating utility and privacy does not exist at the moment. So, we introduced a compilation of the most recent methods for evaluating privacy and utility into a single executable in order to create a report of the similarities and potential privacy breaches between two datasets, whether it is related to synthetic or not. We catalogued 24 different methods, from qualitative to quantitative, column-wise or table-wise evaluations. We hope this resource can help scientists and industries get a better grasp of the synthetic data they have and produce more easily and a better basis to create a new, more broad method for evaluating dataset similarities.

Helping early obstructive sleep apnea diagnosis with machine learning: A systematic review (Preprint)

Background

American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.

Objective

We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.

Methods

We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.

Results

Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.

Conclusions

Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.

Trial Registration

PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339

Partial Multiple Imputation with Variational Autoencoders: Tackling Not at Randomness in Healthcare Data

Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

Impact in the quality of life of parents of children with chronic diseases using psychoeducational interventions - A systematic review with meta-analysis

OBJECTIVE

This study aimed to identify psychoeducational interventions applied to parents of children with chronic diseases and evaluate their impact on their quality of life (QoL).

METHODS

It was conducted in six databases, complemented by references from the included studies and other reviews, manual search, and contact with experts. We included primary studies on parents of children with chronic diseases that studied psychoeducational interventions versus standard care.

RESULTS

We screened 6604 titles and abstracts, reviewed the full text of 60 records, and included 37 primary studies. Half of the studies were on Asthma. We found three intervention formats: one-to-one (43%), groups (49%), and combined approach with individual and group settings (8%). More than 60% of the included studies found statistically significant differences between the intervention and the control group (p < 0.05).

CONCLUSION

Several interventions have shown efficacy in improving parental QoL. Despite that, there is insufficient evidence of interventions' implementation.

PRACTICE IMPLICATIONS

A holistic approach encompassing the patient and the family's biopsychosocial dimensions is fundamental in successfully managing chronic disease in children. It is vital to design and implement interventions accommodating the common issues experienced by children, parents, and families that deal with chronic childhood conditions. Systematic review registration number PROSPERO 2018 CRD42018092135.

Process Evaluation of a Mixed Methods Feasibility Study to Identify Hospital Patients with Palliative Care Needs in Portugal

INTRODUCTION

Evidence shows most patients are not recognised by their attending healthcare professionals as having palliative needs. This feasibility study aimed to aid healthcare professionals identify hospital patients with palliative needs.

MATERIAL AND METHODS

Mixed-methods, cross-sectional, observational study. The patient inclusion criteria comprised: age over 18 years old, being mentally capable to give consent judged as such by participating healthcare professionals, and if unable, having a legal substitute to consent, having a diagnosis of an incurable, potentially life-threatening illness. Field notes were taken for reflexive purposes. Outcome measures included: Integrated Palliative Care Outcome scale, surprise question, phase of illness, referral request status, The Eastern Cooperative Oncology Group Performance Status and social needs assessment. An interim data collection period meeting assessed implementation outcomes in each context. A web-based survey was sent to all participating healthcare professionals at the end of data collection period to explore overall experiences of participation and implementation outcomes.

RESULTS

Forty-two departments in four hospitals were contacted. The study was presented in nine departments. The field notes were vital to understand the recruitment process and difficulties experienced: time constraints, fear of additional work, department dynamics and organisation, relationships between departments and need of training in palliative care and research. One department agreed to participate. There were six participating healthcare professionals and only 45 patients included. Three participating healthcare professionals responded to the web-based survey.

DISCUSSION

The response rate was very low. Legislating palliative care is not enough, and an integrated palliative care plan needs to be implemented at country and institution level.

CONCLUSION

There is an urgent need to provide generalist palliative care training to clinicians.

Prospective international validation of the predisposition, infection, response and organ dysfunction (PIRO) clinical staging system among intensive care and general ward patients

Background

Stratifying patients with sepsis was the basis of the predisposition, infection, response and organ dysfunction (PIRO) concept, an attempt to resolve the heterogeneity in treatment response. The purpose of this study is to perform an independent validation of the PIRO staging system in an international cohort and explore its utility in the identification of patients in whom time to antibiotic treatment is particularly important.

Methods

Prospective international cohort study, conducted over a 6-month period in five Portuguese hospitals and one Australian institution. All consecutive adult patients admitted to selected wards or the intensive care, with infections that met the CDC criteria for lower respiratory tract, urinary, intra-abdominal and bloodstream infections were included.

Results

There were 1638 patients included in the study. Patients who died in hospital presented with a higher PIRO score (10 ± 3 vs 8 ± 4, p < 0.001). The observed mortality was 3%, 15%, 24% and 34% in stage I, II, III and IV, respectively, which was within the predicted intervals of the original model, except for stage IV patients that presented a lower mortality. The hospital survival rate was 84%. The application of the PIRO staging system to the validation cohort resulted in a positive predictive value of 97% for stage I, 91% for stage II, 85% for stage III and 66% for stage IV. The area under the receiver operating characteristics curve (AUROC) was 0.75 for the all cohort and 0.70 if only patients with bacteremia were considered. Patients in stage III and IV who did not have antibiotic therapy administered within the desired time frame had higher mortality rate than those who have timely administration of antibiotic.

Conclusions

To our knowledge, this is the first external validation of this PIRO staging system and it performed well on different patient wards within the hospital and in different types of hospitals. Future studies could apply the PIRO system to decision-making about specific therapeutic interventions and enrollment in clinical trials based on disease stage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00966-7.

COVID-19 surveillance data quality issues: a national consecutive case series

OBJECTIVES

High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions.

SETTINGS

On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained.

PARTICIPANTS

All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June.

PRIMARY AND SECONDARY OUTCOME MEASURES

Data completeness and consistency.

RESULTS

DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.

CONCLUSIONS

Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.

Obstructive sleep apnea: A categorical cluster analysis and visualization

INTRODUCTION AND OBJECTIVES

Obstructive sleep apnea (OSA) is a prevalent sleep condition which is very heterogeneous although not formally characterized as such, resulting in missed or delayed diagnosis. Cluster analysis has been used in different clinical domains, particularly within sleep disorders. We aim to understand OSA heterogeneity and provide a variety of cluster visualizations to communicate the information clearly and efficiently.

MATERIALS AND METHODS

We applied an extension of k-means to be used in categorical variables: k-modes, to identify OSA patients' groups, based on demographic, physical examination, clinical history, and comorbidities characterization variables (n = 40) obtained from a derivation and validation cohorts (211 and 53, respectively) from the northern region of Portugal. Missing values were imputed with k-nearest neighbours (k-NN) and a chi-square test was held for feature selection.

RESULTS

Thirteen variables were inserted in phenotypes, resulting in the following three clusters: Cluster 1, middle-aged males reporting witnessed apneas and high alcohol consumption before sleep; Cluster 2, middle-aged women with increased neck circumference (NC), non-repairing sleep and morning headaches; and Cluster 3, obese elderly males with increased NC, witnessed apneas and alcohol consumption. Patients from the validation cohort assigned to different clusters showed similar proportions when compared with the derivation cohort, for mild (C1: 56 vs 75%, P = 0.230; C2: 61 vs 75%, P = 0.128; C3: 45 vs 48%, P = 0.831), moderate (C1: 24 vs 25%; C2: 20 vs 25%; C3: 25 vs 19%) and severe (C1: 20 vs 0%; C2: 18 vs 0%; C3: 29 vs 33%) levels. Therefore, the allocation supported the validation of the obtained clusters.

CONCLUSIONS

Our findings suggest different OSA patients' groups, creating the need to rethink these patients' stereotypical baseline characteristics.

Identifying and Addressing the Underlying Core Problems in Healthcare Environments: An Illustration Using an Emergency Department Game

The emergency department (ED) crowding is a critical healthcare issue worldwide that leads to long waits and poorer healthcare outcomes. Goldratt's theory of constraints (TOC) has been used effectively to improve such problematic environments for more than three decades. While most TOC solutions are simple, with many viewing them as purely common sense, they represent paradigm shifts in how to manage complex, uncertain, and silo environments. Goldratt used a simple dice game with a straight flow (I-shape) to illustrate the impact of dependent resources and statistical fluctuations in managing resources. Additionally, games help to overcome resistance to change and gain ownership by having participants develop their solutions. This new cooperative game illustrates an ED environment where patients may follow different care pathways according to their clinical needs, timeliness of care is measured in minutes, the demand is highly uncertain, and treatment must frequently start almost immediately. A Monte Carlo simulation validated the TOC solution to this ED game, achieving results similar to the real TOC's implementations. Moreover, this article provides a thorough process to Socratically introduce TOC to healthcare professionals and others to recognize that the EDs' (like other healthcare systems') core problem is the traditional approach to managing them.

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as sociodemographic determinants, self-management ability or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.

OBJECTIVE

The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient-level characteristics retrieved from a population health linked dataset.

SAMPLE AND DESIGN

Retrospective cohort study of patients with diagnosis of T2DM on 1 January 2015, with a 5-year follow-up. Anonymised electronic healthcare records from the Whole System Integrated Care (WSIC) database will be used.

PRELIMINARY OUTCOMES

Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease or death. Predictor variables will include sociodemographic and geographic data, patients' ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multidependence Bayesian networks. The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic curve in the derivation cohort with those calculated from a leave-one-out and a 10 times twofold cross-validation.

ETHICS AND DISSEMINATION

The study has received approvals from the Information Governance Committee at the WSIC. Results will be made available to people with T2DM, their caregivers, the funders, diabetes care societies and other researchers.

Identification of hospitalized patients with community-acquired infection in whom treatment guidelines do not apply: a validated model

OBJECTIVES

To develop and validate a clinical model to identify patients admitted to hospital with community-acquired infection (CAI) caused by pathogens resistant to antimicrobials recommended in current CAI treatment guidelines.

METHODS

International prospective cohort study of consecutive patients admitted with bacterial infection. Logistic regression was used to associate risk factors with infection by a resistant organism. The final model was validated in an independent cohort.

RESULTS

There were 527 patients in the derivation and 89 in the validation cohort. Independent risk factors identified were: atherosclerosis with functional impairment (Karnofsky index <70) [adjusted OR (aOR) (95% CI) = 2.19 (1.41-3.40)]; previous invasive procedures [adjusted OR (95% CI) = 1.98 (1.28-3.05)]; previous colonization with an MDR organism (MDRO) [aOR (95% CI) = 2.67 (1.48-4.81)]; and previous antimicrobial therapy [aOR (95% CI) = 2.81 (1.81-4.38)]. The area under the receiver operating characteristics (AU-ROC) curve (95% CI) for the final model was 0.75 (0.70-0.79). For a predicted probability ≥22% the sensitivity of the model was 82%, with a negative predictive value of 85%. In the validation cohort the sensitivity of the model was 96%. Using this model, unnecessary broad-spectrum therapy would be recommended in 30% of cases whereas undertreatment would occur in only 6% of cases.

CONCLUSIONS

For patients hospitalized with CAI and none of the following risk factors: atherosclerosis with functional impairment; previous invasive procedures; antimicrobial therapy; or MDRO colonization, CAI guidelines can safely be applied. Whereas, for those with some of these risk factors, particularly if more than one, alternative antimicrobial regimens should be considered.

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

BACKGROUND

The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.

OBJECTIVE

This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.

METHODS

A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).

RESULTS

A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).

CONCLUSIONS

Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

COVID-19 and Its Symptoms’ Panoply: A Case-Control Study of 919 Suspected Cases in Locked-Down Ovar, Portugal

Background

Ovar was the first Portuguese municipality to declare active community transmission of SARS-CoV-2, with total lockdown decreed on March 17, 2020. This context provided conditions for a large-scale testing strategy, allowing a referral system considering other symptoms besides the ones that were part of the case definition (fever, cough, and dyspnea). This study aims to identify other symptoms associated with COVID-19 since it may clarify the pre-test probability of the occurrence of the disease.

Methods

This case-control study uses primary care registers between March 29 and May 10, 2020 in Ovar municipality. Pre-test clinical and exposure-risk characteristics, reported by physicians, were collected through a form, and linked with their laboratory result.

Results

The study population included a total of 919 patients, of whom 226 (24.6%) were COVID-19 cases and 693 were negative for SARS-CoV-2. Only 27.1% of the patients reporting contact with a confirmed or suspected case tested positive. In the multivariate analysis, statistical significance was obtained for headaches (OR 0.558), odynophagia (OR 0.273), anosmia (OR 2.360), and other symptoms (OR 2.157). The interaction of anosmia and odynophagia appeared as possibly relevant with a borderline statistically significant OR of 3.375.

Conclusion

COVID-19 has a wide range of symptoms. Of the myriad described, the present study highlights anosmia itself and calls for additional studies on the interaction between anosmia and odynophagia. Headaches and odynophagia by themselves are not associated with an increased risk for the disease. These findings may help clinicians in deciding when to test, especially when other diseases with similar symptoms are more prevalent, namely in winter.

CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens

Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.

Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.

Outcomes of managing healthcare services using the Theory of Constraints: A systematic review

Despite ever-increasing resources devoted to healthcare, lack of capacity and timeliness are still chronic problems worldwide. This systematic review aims to present an overview of the Theory of Constraints (TOC) implementations in healthcare services and their outcomes. We analysed 42 TOC implementations (15 full-text articles, 12 video proceedings, and 2 theses/disserations) from major scientific electronic databases and TOC International Certification Organization Conferences. All implementations reported positive outcomes, both tangible and intangible. The two main improvements reported by authors were in productivity (98%; n = 41) - more patients treated - and in the timeliness of care (83%; n = 35). Furthermore, the selected studies reported dramatic improvements: 50% mean reductions in patient waiting time; 38% reduction in patient length of stay; 43% mean increase in operating room productivity and 34% mean increase in throughput. TOC implementations attained positive results in all levels of the health and social care chain. Most TOC recommendations and changes showed almost immediate results and required little or no additional cost to implement. Evidence supports TOC as a promising solution for the chronic healthcare problem, improving quality and timeliness, both necessary conditions for providing effective healthcare.

SAC Assessment Tool in Implant Dentistry: Evaluation of the Agreement Level Between Users

PURPOSE

The SAC Assessment Tool is a clinical decision support system based on the foundations of the SAC Classification System in Implant Dentistry developed by the International Team for Implantology in 2009. It objectively classifies a patient's rehabilitation with dental implants as straightforward, advanced, or complex, from both a surgical and restorative perspective. The aim of this research was to test the agreement between observers with different qualification levels and clinical experience when using this clinical decision support system as a method that mitigates risk.

MATERIALS AND METHODS

A total of 30 patients were randomly selected from clinical records, and diagnostic casts, intraoral and extraoral images, and panoramic radiographs were obtained. All data were analyzed with and without the SAC Assessment Tool by a dentist with advanced training and clinical experience in implant dentistry (control dentist) and compared with three colleagues (dentists 1, 2, and 3) with fewer qualifications and less clinical experience. All data were analyzed using statistical agreement tests (Fless kappa), interclass correlation, and agreement rate. The level of significance (α) was set at .05.

RESULTS

All patients included in this research presented 104 edentulous areas, which were subjected to surgical evaluation for possible placement of dental implants. Concerning the degree of risk evaluation for dental implant treatment, the results of this study found that the agreement rate of the control dentist without SAC and control dentist with SAC was excellent (81.7%); the agreement rate of the control dentist and dentists 1, 2, and 3 with the use of SAC was satisfactory (67.3% to 76.0%); the variable that presented a lower agreement rate (34.6%) was the comparison between dentists 1, 2, and 3 without use of the SAC Assessment Tool.

CONCLUSION

The SAC classification seems to be a useful tool to assist dentists with less experience in implant dentistry with defining the complexity of the treatment and hence with patient selection. It helps in the collection and homogenization of important clinical data to assess the risk of implant-based rehabilitations, thus contributing to an increase in the agreement rate.

Determining the prevalence of palliative needs and exploring screening accuracy of depression and anxiety items of the integrated palliative care outcome scale - a multi-centre study

Background

patients with palliative needs often experience high symptom burden which causes suffering to themselves and their families. Depression and psychological distress should not be considered a “normal event” in advanced disease patients and should be screened, diagnosed, acted on and followed-up. Psychological distress has been associated with greater physical symptom severity, suffering, and mortality in cancer patients. A holistic, but short measure should be used for physical and non-physical needs assessment. The Integrated Palliative care Outcome Scale is one such measure. This work aims to determine palliative needs of patients and explore screening accuracy of two items pertaining to psychological needs.

Methods

multi-centred observational study using convenience sampling. Data were collected in 9 Portuguese centres. Inclusion criteria: ≥18 years, mentally fit to give consent, diagnosed with an incurable, potentially life-threatening illness. Exclusion criteria: patient in distress (“unable to converse for a period of time”), cognitively impaired. Descriptive statistics used for demographics. Receiving Operator Characteristics curves and Area Under the Curve for anxiety and depression discriminant properties against the Hospital Anxiety and Depression Scale.

Results

1703 individuals were screened between July 1st, 2015 and February 2016. A total of 135 (7.9%) were included. Main reason for exclusion was being healthy (75.2%). The primary care centre screened most individuals, as they have the highest rates of daily patients and the majority are healthy. Mean age is 66.8 years (SD 12.7), 58 (43%) are female. Most patients had a cancer diagnosis 109 (80.7%). Items scoring highest (=4) were: family or friends anxious or worried (36.3%); feeling anxious or worried about illness (13.3%); feeling depressed (9.6%). Using a cut-off score of 2/3, Area Under the Curve for depression and anxiety items were above 70%.

Conclusions

main palliative needs were psychological, family related and spiritual. This suggests that clinical teams may better manage physical issues and there is room for improvement regarding non-physical needs. Using the Integrated Palliative care Outcome Scale systematically could aid clinical teams screening patients for distressing needs and track their progress in assisting patients and families with those issues.

Development and Validation of Risk Matrices Concerning Ulcerative Colitis Outcomes-Bayesian Network Analysis

BACKGROUND

Ulcerative colitis [UC] is a chronic inflammatory disease often accompanied by severe and distressing symptoms that, in some patients, might require a surgical intervention [colectomy]. This study aimed at determining the risk of experiencing progressive disease or requiring colectomy.

MATERIAL AND METHODS

This was a multicentre study: patients' data [n = 1481] were retrieved from the Portuguese database of inflammatory bowel disease patients. Bayesian networks and logistic regression were used to build risk matrices concerning the outcomes of interest.

RESULTS

The derivation cohort included a total of 1210 patients, of whom 6% required a colectomy and 37% had progressive disease [over a median follow-up period of 12 syears]. The risk matrices show that previously hospitalised patients with extensive disease, who are not on immunomodulators and who are refractory to corticosteroid treatment, are the ones at the highest risk of undergoing a colectomy [88%]; whereas male patients, with extensive disease and less than 40 years old at diagnosis, are the ones at the highest risk of experiencing progressive disease [72%]. These results were internally and externally validated, and the AUC [area under the curve] of the ROC [receiver operating characteristic] analysis for the derivation cohort yielded a high discriminative power [92% for colectomy and 72% for progressive disease].

CONCLUSIONS

This study allowed the construction of risk matrices that can be used to accurately predict a UC patient's likelihood of requiring a colectomy or of facing progressive disease, and can be used to individualise therapeutic strategies.

Predicting Blood Donations in a Tertiary Care Center Using Time Series Forecasting

The current algorithm to support platelets stock management assumes that there are always sufficient whole blood donations (WBD) to produce the required amount of pooled platelets. Unfortunately, blood donation rate is uncertain so there is the need to backup pooled platelets productions with single-donor (apheresis) collections to compensate periods of low WBD. The aim of this work was to predict the daily number of WBD to a tertiary care center to preemptively account for a decrease of platelets production. We have collected 62,248 blood donations during 3 years, the daily count of which was used to feed (standalone and ensemble versions of) six prediction models, which were evaluated using the Mean Absolute Error (MAE). Forecast models have shown better performances with a MAE of about 8.6 donations, 34% better than using means or medians alone. Trend lines of donations are better modeled by autoregressive integrated moving average (ARIMA) using a frequency of 365 days, the trade-off being the need for at least two years of data.

Three controversies in health data science

The routine operation of modern healthcare systems produces a wealth of data in electronic health records, administrative databases, clinical registries, and other clinical systems. It is widely acknowledged that there is great potential for utilising these routine data for health research to derive new knowledge about health, disease, and treatments. However, the reuse of routine healthcare data for research is not beyond debate. In this paper, we discuss three issues that have stirred considerable controversy among health data scientists. First, we discuss van der Lei's 1st Law of Medical Informatics, which states that data shall be used only for the purpose for which they were collected. Then, we discuss to which extent routine data sources and innovations in analytical methods alleviate the need to conduct randomised clinical trials. Finally, we address questions of governance, privacy, and trust when routine health data are made available for research. While we don't think that there is a definite "right answer" for any of these issues, we argue that data scientists should be aware of the arguments for different viewpoints, respect their validity, and contribute constructively to the debate. The three controversies discussed in this paper relate to core challenges for research with health data and define an essential research agenda for the health data science community.

Sifting Through Chaos: Extracting Information from Unstructured Legal Opinions

Abiding to the law is, in some cases, a delicate balance between the rights of different players. Re-using health records is such a case. While the law grants reuse rights to public administration documents, in which health records produced in public health institutions are included, it also grants privacy to personal records. To safeguard a correct usage of data, public hospitals in Portugal employ jurists that are responsible for allowing or withholding access rights to health records. To help decision making, these jurists can consult the legal opinions issued by the national committee on public administration documents usage. While these legal opinions are of undeniable value, due to their doctrine contribution, they are only available in a format best suited from printing, forcing individual consultation of each document, with no option, whatsoever of clustered search, filtering or indexing, which are standard operations nowadays in a document management system. When having to decide on tens of data requests a day, it becomes unfeasible to consult the hundreds of legal opinions already available. With the objective to create a modern document management system, we devised an open, platform agnostic system that extracts and compiles the legal opinions, ex-tracts its contents and produces metadata, allowing for a fast searching and filtering of said legal opinions.

Impact of Imputing Missing Data in Bayesian Network Structure Learning for Obstructive Sleep Apnea Diagnosis

Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis.

Phenotyping Obstructive Sleep Apnea Patients: A First Approach to Cluster Visualization

The varied phenotypes of obstructive sleep apnea (OSA) poses critical challenges, resulting in missed or delayed diagnosis. In this work, we applied k-modes, aiming to identify groups of OSA patients, based on demographic, physical examination, clinical history, and comorbidities characterization variables (n = 41) collected from 318 patients. Missing values were imputed with k-nearest neighbours (k-NN) and chi-square test was held. Thirteen variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 were middle-aged men, while Cluster 3 were the oldest men and Cluster 2 mainly middle-aged women. Cluster 3 weighted the most, whereas Cluster 1 weighted the least. The same effect was described in increased neck circumference. The percentages of variables driving sleepiness, congestive heart failure, arrhythmias and pulmonary hypertension were very low (&lt;20%) and OSA severity was more common in mild level. Our results suggest that it is possible to phenotype OSA patients in an objective way, as also, different (although not considered innovative) visualizations improve the recognition of this common sleep pathology.

Predicting outcomes of gastric endoscopic submucosal dissection using a Bayesian approach: a step for individualized risk assessment

BACKGROUND AND STUDY AIMS

Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (ESD).

PATIENTS AND METHODS

Data from 245 gastric ESD were collected, including patient and lesion factors. The two endpoints were curative resection and post-procedural bleeding (PPB). Logistic regression and Bayesian networks were built for each outcome; their predictive value was evaluated in-sample and validated through leave-one-out and cross-validation. Clinical decision support was enhanced by the definition of risk matrices, direct use of Bayesian inference software and by a developed online platform.

RESULTS

ESD was curative in 85.3 % and PPB occurred in 7.7 % of patients. In univariate analysis, male sex, ASA status, carcinoma histology, polypoid or depressed morphology, and lesion size ≥ 20 mm were associated with non-curative resection, while ASA status, antithrombotics and lesion size ≥ 20 mm were associated with PPB. Naïve Bayesian models presented AUROCs of ~80 % in the derivation cohort and ≥ 74 % in cross-validation for both outcomes. Risk matrices were computed, showing that lesions with cancer at biopsies, ≥ 20 mm, proximal or in the middle third, and polypoid are more prone to non-curative resection. PPB risk was < 5 % in lesions < 20 mm in the absence of antithrombotics.

CONCLUSIONS

The derived Bayesian model presented good discriminative power in the prediction of ESD outcomes and can be used to predict individualized probabilities, improving patient information and supporting clinical and management decisions.

Development and Validation of Risk Matrices for Crohn's Disease Outcomes in Patients Who Underwent Early Therapeutic Interventions

INTRODUCTION

The establishment of prognostic models for Crohn's disease [CD] is highly desirable, as they have the potential to guide physicians in the decision-making process concerning therapeutic choices, thus improving patients' health and quality of life. Our aim was to derive models for disabling CD and reoperation based solely on clinical/demographic data.

METHODS

A multicentric and retrospectively enrolled cohort of CD patients, subject to early surgery or immunosuppression, was analysed in order to build Bayesian network models and risk matrices. The final results were validated internally and with a multicentric and prospectively enrolled cohort.

RESULTS

The derivation cohort included a total of 489 CD patients [64% with disabling disease and 18% who needed reoperation], while the validation cohort included 129 CD patients with similar outcome proportions. The Bayesian models achieved an area under the curve of 78% for disabling disease and 86% for reoperation. Age at diagnosis, perianal disease, disease aggressiveness and early therapeutic decisions were found to be significant factors, and were used to construct user-friendly matrices depicting the probability of each outcome in patients with various combinations of these factors. The matrices exhibit good performance for the most important criteria: disabling disease positive post-test odds = 8.00 [2.72-23.44] and reoperation negative post-test odds = 0.02 [0.00-0.11].

CONCLUSIONS

Clinical and demographical risk factors for disabling CD and reoperation were determined and their impact was quantified by means of risk matrices, which are applicable as bedside clinical tools that can help physicians during therapeutic decisions in early disease management.

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis

Introduction

Crohn’s disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients’ risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables.

Materials and methods

This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables.

Results

Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50–4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09–0.25] and 0.50 [0.24–1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation.

Conclusions

The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Clinical predictors of colectomy in patients with ulcerative colitis: systematic review and meta-analysis of cohort studies

INTRODUCTION

Colectomy is a major event that may significantly affect the outcome of ulcerative colitis (UC) in terms of both quality of life and mortality. This paper aims to identify clinical prognostic factors that may be significantly associated with this event.

METHODS

PubMed, ISI Web of Knowledge and Scopus were searched to identify studies investigating the association between clinical factors in adult patients with UC and studied events.The clinical factors evaluated in this meta-analysis were gender, smoking habits, disease extent,use of corticosteroids, and episodes of hospitalization.

RESULTS

Of the 3753 initially selected papers, 20 were included. The analysis showed a significantly lower risk of colectomy for female patients (odds ratio [OR] 0.78 [95% CI 0.68, 0.90]) and for smoking patients (OR 0.55 [0.33, 0.91]), and a higher risk for patients with extensive disease (OR 3.68 [2.39,5.69]), for patients who took corticosteroids at least once (OR 2.10 [1.05, 4.22]), and for patients who were hospitalized (OR 4.13 [3.23, 5.27]).

CONCLUSION

Gender, smoking habits, disease extent, need for corticosteroids, and hospitalization were all significantly associated with UC prognosis. These results may clarify the relative influences of these and other prognostic factors in the natural course of the disease and therefore help improve the management approach, thus improving the follow-up of patients.

Development and assessment of an e-learning course on breast imaging for radiographers: a stratified randomized controlled trial

BACKGROUND

Mammography is considered the best imaging technique for breast cancer screening, and the radiographer plays an important role in its performance. Therefore, continuing education is critical to improving the performance of these professionals and thus providing better health care services.

OBJECTIVE

Our goal was to develop an e-learning course on breast imaging for radiographers, assessing its efficacy, effectiveness, and user satisfaction.

METHODS

A stratified randomized controlled trial was performed with radiographers and radiology students who already had mammography training, using pre- and post-knowledge tests, and satisfaction questionnaires. The primary outcome was the improvement in test results (percentage of correct answers), using intention-to-treat and per-protocol analysis.

RESULTS

A total of 54 participants were assigned to the intervention (20 students plus 34 radiographers) with 53 controls (19+34). The intervention was completed by 40 participants (11+29), with 4 (2+2) discontinued interventions, and 10 (7+3) lost to follow-up. Differences in the primary outcome were found between intervention and control: 21 versus 4 percentage points (pp), P<.001. Stratified analysis showed effect in radiographers (23 pp vs 4 pp; P=.004) but was unclear in students (18 pp vs 5 pp; P=.098). Nonetheless, differences in students' posttest results were found (88% vs 63%; P=.003), which were absent in pretest (63% vs 63%; P=.106). The per-protocol analysis showed a higher effect (26 pp vs 2 pp; P<.001), both in students (25 pp vs 3 pp; P=.004) and radiographers (27 pp vs 2 pp; P<.001). Overall, 85% were satisfied with the course, and 88% considered it successful.

CONCLUSIONS

This e-learning course is effective, especially for radiographers, which highlights the need for continuing education.

Analysis of the quality of hospital information systems Audit Trails

Background

Audit Trails (AT) are fundamental to information security in order to guarantee access traceability but can also be used to improve Health information System’s (HIS) quality namely to assess how they are used or misused. This paper aims at analysing the existence and quality of AT, describing scenarios in hospitals and making some recommendations to improve the quality of information.

Methods

The responsibles of HIS for eight Portuguese hospitals were contacted in order to arrange an interview about the importance of AT and to collect audit trail data from their HIS. Five institutions agreed to participate in this study; four of them accepted to be interviewed, and four sent AT data. The interviews were performed in 2011 and audit trail data sent in 2011 and 2012. Each AT was evaluated and compared in relation to data quality standards, namely for completeness, comprehensibility, traceability among others. Only one of the AT had enough information for us to apply a consistency evaluation by modelling user behaviour.

Results

The interviewees in these hospitals only knew a few AT (average of 1 AT per hospital in an estimate of 21 existing HIS), although they all recognize some advantages of analysing AT. Four hospitals sent a total of 7 AT – 2 from Radiology Information System (RIS), 2 from Picture Archiving and Communication System (PACS), 3 from Patient Records. Three of the AT were understandable and three of the AT were complete. The AT from the patient records are better structured and more complete than the RIS/PACS.

Conclusions

Existing AT do not have enough quality to guarantee traceability or be used in HIS improvement. Its quality reflects the importance given to them by the CIO of healthcare institutions. Existing standards (e.g. ASTM:E2147, ISO/TS 18308:2004, ISO/IEC 27001:2006) are still not broadly used in Portugal.

Predisposition, insult/infection, response and organ dysfunction (PIRO): a pilot clinical staging system for hospital mortality in patients with infection

PURPOSE

To develop a clinical staging system based on the PIRO concept (Predisposition, Infection, RESPONSE and Organ dysfunction) for hospitalized patients with infection.

METHODS

One year prospective cohort study of all hospitalized patients with infection (n = 1035), admitted into a large tertiary care, university hospital. Variables associated with hospital mortality were selected using logistic regressions. Based on the regression coefficients, a score for each PIRO component was developed and a classification tree was used to stratify patients into four stages of increased risk of hospital mortality. The final clinical staging system was then validated using an independent cohort (n = 186).

RESULTS

Factors significantly associated with hospital mortality were • for Predisposition: age, sex, previous antibiotic therapy, chronic hepatic disease, chronic hematologic disease, cancer, atherosclerosis and a Karnofsky index<70; • for Insult/Infection: type of infection • for

RESPONSE

abnormal temperature, tachypnea, hyperglycemia and severity of infection and • for Organ dysfunction: hypotension and SOFA score≥1. The area under the ROC curve (CI95%) for the combined PIRO model as a predictor for mortality was 0.85 (0.82-0.88). Based on the scores for each of the PIRO components and on the cut-offs estimated from the classification tree, patients were stratified into four stages of increased mortality rates: stage I: ≤5%, stage II: 6-20%, stage III: 21-50% and stage IV: >50%. Finally, this new clinical staging system was studied in a validation cohort, which provided similar results (0%, 9%, 31% and 67%, in each stage, respectively).

CONCLUSIONS

Based on the PIRO concept, a new clinical staging system was developed for hospitalized patients with infection, allowing stratification into four stages of increased mortality, using the different scores obtained in Predisposition, RESPONSE, Infection and Organ dysfunction. The proposed system will likely help to define inclusion criteria in clinical trials as well as tailoring individual management plans for patients with infection.