A Review of Mortality Risk Prediction Models in Smartphone Applications.

Healthcare professionals in healthcare systems need access to freely available, real-time, evidence-based mortality risk prediction smartphone applications to facilitate resource allocation. The objective of this study is to evaluate the quality of smartphone mobile health applications that include mortality prediction models, and corresponding information quality. We conducted a systematic review of commercially available smartphone applications in Google Play for Android, and iTunes for iOS smartphone applications. We performed initial screening, data extraction, and rated smartphone application quality using the Mobile Application Rating Scale: user version (uMARS). The information quality of smartphone applications was evaluated using two patient vignettes, representing low and high risk of mortality, based on critical care data from the Medical Information Mart for Intensive Care (MIMIC) III database. Out of 3051 evaluated smartphone applications, 33 met our final inclusion criteria. We identified 21 discrete mortality risk prediction models in smartphone applications. The most common mortality predicting models were Sequential Organ Failure Assessment (SOFA) (n = 15) and Acute Physiology and Clinical Health Assessment II (n = 13). The smartphone applications with the highest quality uMARS scores were Observation-NEWS 2 (4.64) for iOS smartphones, and MDCalc Medical Calculator (4.75) for Android smartphones. All SOFA-based smartphone applications provided consistent information quality with the original SOFA model for both the low and high-risk patient vignettes. We identified freely available, high-quality mortality risk prediction smartphone applications that can be used by healthcare professionals to make evidence-based decisions in critical care environments.

Using generative artificial intelligence in bibliometric analysis: 10 years of research trends from the European Resuscitation Congresses.

Aims: The aim of this study is to use generative artificial intelligence to perform bibliometric analysis on abstracts published at European Resuscitation Council (ERC) annual scientific congress and define trends in ERC guidelines topics over the last decade. Methods: In this bibliometric analysis, the WebHarvy software (SysNucleus, India) was used to download data from the Resuscitation journal's website through the technique of web scraping. Next, the Chat Generative Pre-trained Transformer 4 (ChatGPT-4) application programming interface (Open AI, USA) was used to implement the multinomial classification of abstract titles following the ERC 2021 guidelines topics. Results: From 2012 to 2022 a total of 2491 abstracts have been published at ERC congresses. Published abstracts ranged from 88 (in 2020) to 368 (in 2015). On average, the most common ERC guidelines topics were Adult basic life support (50.1%), followed by Adult advanced life support (41.5%), while Newborn resuscitation and support of transition of infants at birth (2.1%) was the least common topic. The findings also highlight that the Basic Life Support and Adult Advanced Life Support ERC guidelines topics have the strongest co-occurrence to all ERC guidelines topics, where the Newborn resuscitation and support of transition of infants at birth (2.1%; 52/2491) ERC guidelines topic has the weakest co-occurrence. Conclusion: This study demonstrates the capabilities of generative artificial intelligence in the bibliometric analysis of abstract titles using the example of resuscitation medicine research over the last decade at ERC conferences using large language models.

Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models.

Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing rapidly. Several models have been built to predict T2DM onset in the future or detect undiagnosed T2DM in patients. Additional to the performance of such models, their interpretability is crucial for health experts, especially in personalized clinical prediction models. Data collected over 42 months from health check-up examinations and prescribed drugs data repositories of four primary healthcare providers were used in this study. We propose a framework consisting of LogicRegression based feature extraction and Least Absolute Shrinkage and Selection operator based prediction modeling for undiagnosed T2DM prediction. Performance of the models was measured using Area under the ROC curve (AUC) with corresponding confidence intervals. Results show that using LogicRegression based feature extraction resulted in simpler models, which are easier for healthcare experts to interpret, especially in cases with many binary features. Models developed using the proposed framework resulted in an AUC of 0.818 (95% Confidence Interval (CI): 0.812-0.823) that was comparable to more complex models (i.e., models with a larger number of features), where all features were included in prediction model development with the AUC of 0.816 (95% CI: 0.810-0.822). However, the difference in the number of used features was significant. This study proposes a framework for building interpretable models in healthcare that can contribute to higher trust in prediction models from healthcare experts.

The Antimicrobial Effect of Various Single-Strain and Multi-Strain Probiotics, Dietary Supplements or Other Beneficial Microbes against Common Clinical Wound Pathogens.

The skin is the largest organ in the human body and is colonized by a diverse microbiota that works in harmony to protect the skin. However, when skin damage occurs, the skin microbiota is also disrupted, and pathogens can invade the wound and cause infection. Probiotics or other beneficial microbes and their metabolites are one possible alternative treatment for combating skin pathogens via their antimicrobial effectiveness. The objective of our study was to evaluate the antimicrobial effect of seven multi-strain dietary supplements and eleven single-strain microbes that contain probiotics against 15 clinical wound pathogens using the agar spot assay, co-culturing assay, and agar well diffusion assay. We also conducted genera-specific and species-specific molecular methods to detect the DNA in the dietary supplements and single-strain beneficial microbes. We found that the multi-strain dietary supplements exhibited a statistically significant higher antagonistic effect against the challenge wound pathogens than the single-strain microbes and that lactobacilli-containing dietary supplements and single-strain microbes were significantly more efficient than the selected propionibacteria and bacilli. Differences in results between methods were also observed, possibly due to different mechanisms of action. Individual pathogens were susceptible to different dietary supplements or single-strain microbes. Perhaps an individual approach such as a 'probiogram' could be a possibility in the future as a method to find the most efficient targeted probiotic strains, cell-free supernatants, or neutralized cell-free supernatants that have the highest antagonistic effect against individual clinical wound pathogens.

Nutritional quality of beverages available in vending machines in health and social care institutions: do we really want such offers?

BACKGROUND: Vending machines represent one way of offering food, but they are overlooked in the efforts to improve people's eating habits. The aim of our study was to analyse the variety and nutritional values of beverages offered in vending machines in social and health care institution in Slovenia. METHODS: The available beverages were quantitatively assessed using traffic light profiling and the model for nutrient profiling used by Food Standards Australia New Zealand. Vending machines in 188 institutions were surveyed, resulting in 3046 different beverages consisting of 162 unique product labels. RESULTS: Between 51 and 54% of beverages were categorised as unhealthy with regard to sugar content. Water accounted for only 13.7% of all beverages in vending machines. About 82% of beverages in vending machines were devoted to sugar-sweetened beverages, the majority (58.9%) presented in 500-ml bottles. The average sugar content and average calories in beverages sold in vending machines are slightly lower than in beverages sold in food stores. CONCLUSIONS: We suggest that regulatory guidelines should be included in the tender conditions for vending machines in health and social care institutions, to ensure healthy food and beverage choices.

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

Sweet, Fat and Salty: Snacks in Vending Machines in Health and Social Care Institutions in Slovenia.

Vending machines in health and social care facilities are often the only possible choice for a quick snack for workers and visitors, in many cases providing unhealthy dietary choices. Our study aimed to analyse the variety and nutritional quality of foods available in vending machines placed in social and health care institution in Slovenia. The available snacks were quantitatively assessed, using traffic light profiling. The model used for nutrient profiling was that of the Food Standards Australia New Zealand (FSANZ). Vending machines in 188 institutions were surveyed, resulting in 5625 food-items consisting of 267 unique product labels. Sweet products dominate in vending machines offers (about 70%), while nuts and seeds (8.4%), yoghurts (2.1%), fruits (1.4%) and milk (0.3%) are present in a very small proportion or are not available at all. According to FSANZ, 88.5% of all displayed food items in vending machines can be considered as lower nutritional quality or less healthy products. The authors' future activities will be focused on ensuring wider availability of healthy dietary choices and on including official guidelines in tender conditions for vending machines in health and social care institutions in Slovenia.

An international cross-cultural study of nursing students' perceptions of caring.

BACKGROUND: Single studies suggest that nursing students perceive caring as more an instrumental than expressive behaviour and indicate some differences between caring perceptions in junior and senior nursing students. However, there are limited studies investigating caring perceptions in nursing students across multiple cultures. OBJECTIVE: To determine perceptions of caring in Slovene, Croatian, Chinese and Russian nursing students and explore whether there are statistically significant differences in perceptions of caring between countries and between first and third-year nursing students. DESIGN: A cross-sectional descriptive study design was used. SETTINGS AND PARTICIPANTS: The study included 604 nursing students enrolled in first and third year in seven different nursing faculties in four countries: Slovenia; China; Croatia; and the Russian Federation. METHODS: The 25-item Caring Dimension Inventory (CDI-25) was used to measure caring perceptions. We also included demographic questions regarding age, gender, country, year of study and type of study. Demographic data were analysed using descriptive analysis while a two-way analysis of variance (ANOVA) adjusted for unequal sample sizes was performed together with a post hoc analysis of the results. RESULTS: The results of two-way ANOVA showed that both main effects (country and year of study) were statistically significant, as well as their interaction at the 0.05 significance level. The main effect for country was F(3, 596) = 3.591, p < 0.0136 indicating a significant difference in CDI-25 between Slovenia (M = 108.9, SD = 9.2), Russian Federation (M = 107.1, SD = 8.2), China (M = 102.8, SD = 9.7) and Croatia (M = 110.0, SD = 8.6). CONCLUSIONS: Perceptions of caring in nursing students differ across countries, probably due to different educational systems, curricula, cultural differences and societal values. Implementing caring theories in nursing curricula could help students to cultivate caring during their education.

Challenges associated with missing data in electronic health records: A case study of a risk prediction model for diabetes using data from Slovenian primary care.

The increasing availability of data stored in electronic health records brings substantial opportunities for advancing patient care and population health. This is, however, fundamentally dependant on the completeness and quality of data in these electronic health records. We sought to use electronic health record data to populate a risk prediction model for identifying patients with undiagnosed type 2 diabetes mellitus. We, however, found substantial (up to 90%) amounts of missing data in some healthcare centres. Attempts at imputing for these missing data or using reduced dataset by removing incomplete records resulted in a major deterioration in the performance of the prediction model. This case study illustrates the substantial wasted opportunities resulting from incomplete records by simulation of missing and incomplete records in predictive modelling process. Government and professional bodies need to prioritise efforts to address these data shortcomings in order to ensure that electronic health record data are maximally exploited for patient and population benefit.

Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data.

This study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance.

Sentiment Analysis of Social Media Users' Emotional Response to Sudden Cardiac Arrest During a Football Broadcast.

This case series analyzes social media users' sentiments after successful cardiopulmonary resuscitation of Damar Hamlin following his sudden cardiac arrest on national television.

Building interpretable models for polypharmacy prediction in older chronic patients based on drug prescription records.

BACKGROUND: Multimorbidity presents an increasingly common problem in older population, and is tightly related to polypharmacy, i.e., concurrent use of multiple medications by one individual. Detecting polypharmacy from drug prescription records is not only related to multimorbidity, but can also point at incorrect use of medicines. In this work, we build models for predicting polypharmacy from drug prescription records for newly diagnosed chronic patients. We evaluate the models' performance with a strong focus on interpretability of the results. METHODS: A centrally collected nationwide dataset of prescription records was used to perform electronic phenotyping of patients for the following two chronic conditions: type 2 diabetes mellitus (T2D) and cardiovascular disease (CVD). In addition, a hospital discharge dataset was linked to the prescription records. A regularized regression model was built for 11 different experimental scenarios on two datasets, and complexity of the model was controlled with a maximum number of dimensions (MND) parameter. Performance and interpretability of the model were evaluated with AUC, AUPRC, calibration plots, and interpretation by a medical doctor. RESULTS: For the CVD model, AUC and AUPRC values of 0.900 (95% [0.898-0.901]) and 0.640 (0.635-0.645) were reached, respectively, while for the T2D model the values were 0.808 (0.803-0.812) and 0.732 (0.725-0.739). Reducing complexity of the model by 65% and 48% for CVD and T2D, resulted in 3% and 4% lower AUC, and 4% and 5% lower AUPRC values, respectively. Calibration plots for our models showed that we can achieve moderate calibration with reducing the models' complexity without significant loss of predictive performance. DISCUSSION: In this study, we found that it is possible to use drug prescription data to build a model for polypharmacy prediction in older population. In addition, the study showed that it is possible to find a balance between good performance and interpretability of the model, and achieve acceptable calibration at the same time.