Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis. METHODS: All adult discharges for STEMI in the National Inpatient Sample (October 2015 to December 2019) were included, excluding patients with prior myocardial infarction. Machine-learning clustering analysis was used to define clusters based on 21 clinical attributes of interest. Main outcomes of the study were cluster-based comparison of risk profile, in-hospital clinical outcomes and utilization of invasive management. Binomial hierarchical multivariable logistic regression with adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) was used to detect the between-cluster differences. RESULTS: Out of overall 470,960 STEMI cases, the machine-learning analysis revealed 4 different clusters with 205,640 (cluster 0: 'behavioural risk cluster'), 146,400 (cluster 1: 'least comorbidity cluster'), 45,100 (cluster 2: 'diabetes with end-organ damage cluster') and 73,820 (cluster 3: 'cardiometabolic cluster') cases. Attributes with the highest importance for clustering were hypertension and diabetes. After multivariable adjustment, patients from 'diabetes with end-organ damage cluster' exhibited the worst mortality, MACCE and ischemic stroke (p < 0.001 for all), as well as the lowest utilization of invasive management (p < 0.001 for all), in comparison to other clusters. Patients from 'behavioural risk cluster' exhibited the best in-hospital prognosis and the highest utilization of invasive management, compared to other clusters (p < 0.001 for all). CONCLUSIONS: Machine learning driven clustering of inpatients with STEMI reveals important population subgroups with distinct prevalence, risk profile, prognosis and management. Data driven approaches may identify high risk phenogroups and warrants further study.

Machine learning models based on routinely sampled blood tests can predict the presence of malignancy amongst patients with suspected musculoskeletal malignancy.

AIMS: This study explores the possibility of using routinely taken blood tests in the diagnosis and triage of patients with suspected musculoskeletal malignancy. METHODS: A retrospective study was performed on results of patients who had presented for assessment to a regional musculoskeletal tumour unit. Blood results of patients with a histologically confirmed diagnosis between 2010 and 2020 were retrieved. 33 distinct blood tests were available for model forming. Results were standardised by calculating z-scores. Data were split into a training set (70%) and a test set (30%). The training set was balanced by resampling underrepresented classes. The random forest algorithm performed best and was selected for model forming. Receiver operating characteristic curves were used to find the optimum threshold. Models were calibrated and performance metrics evaluated with confusion tables. RESULTS: 2371 patients formed the study population. 1080 had a malignant diagnosis in one of three categories: sarcoma, metastasis, or haematological malignancy. 1291 had a benign condition. Metastasis could be predicted with an accuracy of 79% (AUC 87%, sensitivity 79%, specificity 80% NPV 91%). Haematological malignancy accuracy 79% (AUC 81%, sensitivity 77%, specificity 79%, NPV 97%). Sarcoma accuracy 64% (AUC 73%, sensitivity 76%, specificity 61%, NPV 88%) and all malignancy accuracy 74% (AUC 80%, sensitivity 72%, specificity 75%, NPV 76%). CONCLUSION: Routinely performed blood tests can be useful in triage of musculoskeletal tumours and can be used to predict presence of musculoskeletal malignancy.

Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study.

AIMS: Most adults presenting in primary care with chest pain symptoms will not receive a diagnosis ('unattributed' chest pain) but are at increased risk of cardiovascular events. To assess within patients with unattributed chest pain, risk factors for cardiovascular events and whether those at greatest risk of cardiovascular disease can be ascertained by an existing general population risk prediction model or by development of a new model. METHODS AND RESULTS: The study used UK primary care electronic health records from the Clinical Practice Research Datalink linked to admitted hospitalizations. Study population was patients aged 18 plus with recorded unattributed chest pain 2002-2018. Cardiovascular risk prediction models were developed with external validation and comparison of performance to QRISK3, a general population risk prediction model. There were 374 917 patients with unattributed chest pain in the development data set. The strongest risk factors for cardiovascular disease included diabetes, atrial fibrillation, and hypertension. Risk was increased in males, patients of Asian ethnicity, those in more deprived areas, obese patients, and smokers. The final developed model had good predictive performance (external validation c-statistic 0.81, calibration slope 1.02). A model using a subset of key risk factors for cardiovascular disease gave nearly identical performance. QRISK3 underestimated cardiovascular risk. CONCLUSION: Patients presenting with unattributed chest pain are at increased risk of cardiovascular events. It is feasible to accurately estimate individual risk using routinely recorded information in the primary care record, focusing on a small number of risk factors. Patients at highest risk could be targeted for preventative measures.

It is known that patients with chest pain without a recognized cause are at increased risk of future cardiovascular events (for example, heart disease) and so this study aimed to find out whether those patients at greatest risk could be determined using information in their health records. It is possible to accurately estimate a person's risk of future cardiovascular events using the information entered into their health records, and this risk can be estimated using only a small number of factors.Patients at highest risk could now be targeted for management to help prevent future cardiovascular events.

Effects of Surface Chemistry Interaction on Primary Neural Stem Cell Neurosphere Responses.

The characteristics of a material's surface are extremely important when considering their interactions with biological species. Despite surface chemistry playing a critical role in mediating the responses of cells, there remains no single rule which dictates absolute performance; this is particularly challenging when considering the response of differing cell types to a range of materials. Here, we highlight the functional behavior of neural stem cells presented as neurospheres, with respect to a range of alkane-based self-assembled monolayers presenting different functional groups: OH, CO2H, NH2, phenyl, CH3, SH, and laminin. The influence of chemical cues was examined in terms of neurosphere spreading on each of these defined surfaces (cell adhesion and migration capacity) and neuronal versus glial marker expression. Measurements were made over a time series of 3, 5, and 7 days, showing a dynamic nature to the initial responses observed after seeding. While OH surfaces presented an excellent platform for glial migration, larger proportions of cells expressing neuronal ß3-tubulin were found on SH- and laminin-coated surfaces. Axonal elongation was found to be initially similar on all surfaces with neurite lengths having a wider spread predominantly on NH2- and laminin-presenting surfaces. A generalized trend could not be found to correlate cellular responses with surface wettability, lipophilicity (log P), or charge/ionizability (pK a). These results highlight the potential for chemical cues to direct primary neural stem cell responses in contact with the defined materials. New biomaterials which control specific cell culture characteristics in vitro will streamline the up-scale manufacture of cellular therapies, with the enrichment of the required populations resulting from a defined material interaction.

Improving predictor selection for injury modelling methods in male footballers.

OBJECTIVES: This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. METHODS: Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. RESULTS: The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. CONCLUSION: For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.

Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

Using an evolutionary algorithm to determine the parameters of a biologically inspired model of head direction cells.

A biologically inspired model of head direction cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the host's location in the environment. The head direction system thus acts as a biological compass (though not a magnetic one) for its host. Head direction cells are influenced in different ways by idiothetic (host-centred) and allothetic (not host-centred) cues. The model presented here uses the visual, vestibular and kinesthetic inputs that are simulated by robot sensors. Real robot-sensor data has been used in order to train the model's artificial neural network connections. The main contribution of this paper lies in the use of an evolutionary algorithm in order to determine the values of parameters that determine the behaviour of the model. More importantly, the objective function of the evolutionary strategy used takes into consideration quantitative biological observations reported in the literature.