Machine learning in infection management using routine electronic health records:tools, techniques, and reporting of future technologies

Background: Machine learning (ML) is increasingly being used in many areas of health care. Its use in infection management is catching up as identified in a recent review in this journal. We present here a complementary review to this work. Objectives: To support clinicians and researchers in navigating through the methodological aspects of ML approaches in the field of infection management. Sources: A Medline search was performed with the keywords artificial intelligence, machine learning, infection∗, and infectious disease∗ for the years 2014–2019. Studies using routinely available electronic hospital record data from an inpatient setting with a focus on bacterial and fungal infections were included. Content: Fifty-two studies were included and divided into six groups based on their focus. These studies covered detection/prediction of sepsis (n = 19), hospital-acquired infections (n = 11), surgical site infections and other postoperative infections (n = 11), microbiological test results (n = 4), infections in general (n = 2), musculoskeletal infections (n = 2), and other topics (urinary tract infections, deep fungal infections, antimicrobial prescriptions; n = 1 each). In total, 35 different ML techniques were used. Logistic regression was applied in 18 studies followed by random forest, support vector machines, and artificial neural networks in 18, 12, and seven studies, respectively. Overall, the studies were very heterogeneous in their approach and their reporting. Detailed information on data handling and software code was often missing. Validation on new datasets and/or in other institutions was rarely done. Clinical studies on the impact of ML in infection management were lacking. Implications: Promising approaches for ML use in infectious diseases were identified. But building trust in these new technologies will require improved reporting. Explainability and interpretability of the models used were rarely addressed and should be further explored. Independent model validation and clinical studies evaluating the added value of ML approaches are needed

Rapid and robust phylotyping of spa t003, a dominant MRSA clone in Luxembourg and other European countries

Background: spa typing is a common genotyping tool for methicillin-resistant Staphylococcus aureus (MRSA) in Europe. Given the high prevalence of dominant clones, spa-typing is proving to be limited in its ability to distinguish outbreak isolates from background isolates. New molecular tools need to be employed to improve subtyping of dominant local MRSA strains (e.g., spa type t003). Methods: Phylogenetically critical, or canonical, SNPs (can-SNPs) were identified as subtyping targets through sequence analysis of 40 MRSA whole genomes from Luxembourg. Real-time PCR assays were designed around target SNPs and validated using a repository of 240 previously sub-typed and epidemiologically characterized Luxembourg MRSA isolates, including 153 community and hospital isolates, 69 isolates from long term care (LTC) facilities, and 21 prospectively analyzed MRSA isolates. Selected isolates were also analyzed by whole genome SNP typing (WGST) for comparison to the SNP assays and other subtyping techniques. Results: Fourteen real-time PCR assays were developed and validated, including two assays to determine presence of spa t003 or t008. The other twelve assays successfully provided a high degree of resolution within the t003 subtype. WGST analysis of the LTC facility isolates provided greater resolution than other subtyping tools, identifying clusters indicative of ongoing transmission within LTC facilities. Conclusions: canSNP-based PCR assays are useful for local level MRSA phylotyping, especially in the presence of one or more dominant clones. The assays designed here can be easily adapted for investigating t003 MRSA strains in other regions in Western Europe. WGST provides substantially better resolution than other typing methods

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models. Methods: 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE). Results: The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion: The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies

An ontology-based nurse call management system (oNCS) with probabilistic priority assessment

<p>Abstract</p> <p>Background</p> <p>The current, place-oriented nurse call systems are very static. A patient can only make calls with a button which is fixed to a wall of a room. Moreover, the system does not take into account various factors specific to a situation. In the future, there will be an evolution to a mobile button for each patient so that they can walk around freely and still make calls. The system would become person-oriented and the available context information should be taken into account to assign the correct nurse to a call.</p> <p>The aim of this research is (1) the design of a software platform that supports the transition to mobile and wireless nurse call buttons in hospitals and residential care and (2) the design of a sophisticated nurse call algorithm. This algorithm dynamically adapts to the situation at hand by taking the profile information of staff members and patients into account. Additionally, the priority of a call probabilistically depends on the risk factors, assigned to a patient.</p> <p>Methods</p> <p>The <it>ontology-based Nurse Call System (oNCS) </it>was developed as an extension of a <it>Context-Aware Service Platform</it>. An ontology is used to manage the profile information. Rules implement the novel nurse call algorithm that takes all this information into account. Probabilistic reasoning algorithms are designed to determine the priority of a call based on the risk factors of the patient.</p> <p>Results</p> <p>The <it>oNCS </it>system is evaluated through a prototype implementation and simulations, based on a detailed dataset obtained from Ghent University Hospital. The arrival times of nurses at the location of a call, the workload distribution of calls amongst nurses and the assignment of priorities to calls are compared for the <it>oNCS </it><it>system </it>and the current, place-oriented nurse call system. Additionally, the performance of the system is discussed.</p> <p>Conclusions</p> <p>The execution time of the nurse call algorithm is on average 50.333 ms. Moreover, the <it>oNCS system </it>significantly improves the assignment of nurses to calls. Calls generally have a nurse present faster and the workload-distribution amongst the nurses improves.</p

Attitude towards pre-implantation genetic diagnosis for hereditary cancer

The use of pre-implantation genetic diagnosis (PGD) for hereditary cancer is subject to on-going debate, particularly among professionals. This study evaluates the attitude towards PGD and attitude-associated characteristics of those concerned: family members with a hereditary cancer predisposition. Forty-eight Von Hippel-Lindau and 18 Li–Fraumeni Syndrome families were identified via the 9 family cancer clinics in the Netherlands. In total, 216 high risk family members and partners were approached, of whom 179 (83%) completed a self-report questionnaire. Of the high risk family members, 35% expressed a positive attitude towards PGD. Those with a current desire to have children were significantly more likely to have a positive attitude: 48% would consider the use of PGD. No other sociodemographic, medical or psychosocial variables were associated significantly with a positive attitude. The most frequently reported advantage of PGD is the avoidance of a possible pregnancy termination. Uncertainty about late effects was the most frequently reported disadvantage. These results indicate that approximately half of those contemplating a future pregnancy would consider the use of PGD. The actual uptake, however, is expected to be lower. There is no indication that psychosocial factors affect interest in PGD

A Decade of Genetic Counseling in Frontotemporal Dementia Affected Families: Few Counseling Requests and much Familial Opposition to Testing

A decade of genetic counseling of frontotemporal dementia (FTD) affected families has generated two important observations. First, the uptake rate for presymptomatic testing for FTD is low in our department of Clinical Genetics at the Erasmus Medical Center in the Netherlands. Second, FTD at-risk counselees reported substantial familial opposition to genetic testing, which is distinct from the attitude in Huntington Disease affected families. We hypothesize that the low acceptance for FTD genetic counseling is consequential to the familial opposition and explain this within the theoretical framework of separation-individuation. Furthermore, we hypothesize that separation-individuation problems do not similarly influence the acceptance of HD genetic counseling, due to the educative role of the well-organised patient organization for HD in the Netherlands. We offer counseling recommendations that serve to facilitate the individuation of the counselee with respect to the FTD genetic test

Low-Intensity, High-Frequency Grazing Positively Affects Defoliating Behavior, Nutrient Intake and Blood Indicators of Nutrition and Stress in Sheep

The intensity and frequency of grazing affect the defoliating strategy of ruminants, their daily nutrient intake, thus nutrition and physiological status. Italian ryegrass (Lolium multiflorum Lam.) pastures were grazed by sheep either under a low-intensity/high-frequency grazing strategy (Rotatinuous stocking; RN) with nominal pre- and post-grazing sward heights of 18 and 11 cm, respectively, or under a high-intensity/low-frequency strategy (traditional rotational stocking; RT) with nominal pre- and post-grazing sward heights of 25 and 5 cm, respectively. Treatments were arranged under a complete randomized design and evaluated over two periods, in different years. In 2017, the aim was to depict the type of bites that sheep perform during the grazing-down and associate them to the grazing management strategy according to their relative contribution to the diet ingested. In 2018 we estimated the total nutrient intake and evaluated blood indicators of the nutritional status and immune response to stress of sheep. The bite types accounting the most for the diet ingested by RN sheep were those performed on the “top stratum” of plants with around 20, 15, and 25 cm, whereas the type of bites accounting the most for the diet of RT sheep were those performed on “grazed plants” with around 10, 5, and ≤ 3 cm. In 2018, the RN sheep increased by 18% the total organic matter (OM) intake and by 20–25% the intake of soluble nutrients (i.e., crude protein, total soluble sugars, crude fat), digestible OM and of metabolizable energy, and had 17.5, 18, and 6.1% greater blood concentration of glucose, urea nitrogen (BUN) and albumin, respectively, but 17% lower blood neutrophil-to-lymphocyte (N:L) ratio. Sheep grazing vegetative Italian ryegrass pastures under the low-intensity/high-frequency grazing strategy (RN) ingested a diet of better quality from bites allocated on the top stratum of plants, had greater intake of soluble nutrients and blood parameters positively associated with nutritional status and immune response to stress