Family-Centric Applied Behavior Analysis Facilitates Improved Treatment Utilization and Outcomes

Background/Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong impacts on functional social and daily living skills, and restricted, repetitive behaviors (RRBs). Applied behavior analysis (ABA), the gold-standard treatment for ASD, has been extensively validated. ABA access is hindered by limited availability of qualified professionals and logistical and financial barriers. Scientifically validated, parent-led ABA can fill the accessibility gap by overcoming treatment barriers. This retrospective cohort study examines how our ABA treatment model, utilizing parent behavior technicians (pBTs) to deliver ABA, impacts adaptive behaviors and interfering behaviors (IBs) in a cohort of children on the autism spectrum with varying ASD severity levels, and with or without clinically significant IBs. Methods: Clinical outcomes of 36 patients ages 3-15 years were assessed using longitudinal changes in Vineland-3 after 3+ months of pBT-delivered ABA treatment. Results: Within the pBT model, our patients demonstrated clinically significant improvements in Vineland-3 Composite, domain, and subdomain scores, and utilization was higher in severe ASD. pBTs utilized more prescribed ABA when children initiated treatment with clinically significant IBs, and these children also showed greater gains in their Composite scores. Study limitations include sample size, inter-rater reliability, potential assessment metric bias and schedule variability, and confounding intrinsic or extrinsic factors. Conclusion: Overall, our pBT model facilitated high treatment utilization and showed robust effectiveness, achieving improved adaptive behaviors and reduced IBs when compared to conventional ABA delivery. The pBT model is a strong contender to fill the widening treatment accessibility gap and represents a powerful tool for addressing systemic problems in ABA treatment delivery.

Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease

Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.

Machine Learning Differentiation of Autism Spectrum Sub-Classifications

PURPOSE

Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum.

METHODS

We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data.

RESULTS

The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum.

CONCLUSION

Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.

Technical considerations of endovascular management of true visceral artery aneurysms

BACKGROUND

True visceral artery aneurysms are potentially complex to treat but with advances in technology and increasing interventional radiology expertise over the past decade are now increasingly the domain of the interventional radiologist. BODY: The interventional approach is based on localization of the aneurysm and identification of the anatomical determinants to treat these lesions to prevent aneurysm rupture. Several different endovascular techniques are available and should be selected carefully, dependent on the aneurysm morphology. Standard endovascular treatment options include stent-graft placement and trans-arterial embolisation. Different strategies are divided into parent artery preservation and parent artery sacrifice techniques. Endovascular device innovations now include multilayer flow-diverting stents, double-layer micromesh stents, double-lumen balloons and microvascular plugs and are also associated with high rates of technical success.

CONCLUSION

Complex techniques such as stent-assisted coiling and balloon-remodeling techniques are useful techniques and require advanced embolisation skills and are further described.

Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment

Objective This study examines the implementation of a hybrid applied behavioral analysis (ABA) treatment model to determine its impact on autism spectrum disorder (ASD) patient outcomes.  Methods Retrospective data were collected for 25 pediatric patients to measure progress before and after the implementation of a hybrid ABA treatment model under which therapists consistently captured session notes electronically regarding goals and patient progress. ABA treatment was streamlined for consistent delivery, with improved software utilization for tracking scheduling and progress. Eleven goals within three domains (behavioral, social, and communication) were examined.  Results After the implementation of the hybrid model, the goal success rate improved by 9.7% compared to the baseline; 41.8% of goals showed improvement, 38.4% showed a flat trend, and 19.8% showed deterioration. Multiple goals trended upwards in 76% of the patients.  Conclusion This pilot study demonstrated that enhancing the consistency with which ABA treatment is monitored/delivered can improve patient outcomes as seen through improved attainment of goals.

Post-yield softening of bending-dominated metal metamaterials

Post-yield softening (PYS) plays an important role in guiding the design of high-performance energy-absorbing lattice materials. PYS is usually restricted to lattice materials that are stretching-dominated according to the Gibson-Ashby model. Contrary to this long-held assumption, this work shows that PYS can also occur in various bending-dominated Ti-6Al-4 V lattices with increasing relative density. The underlying mechanism for this unusual property is elucidated using the Timoshenko-beam theory. It is attributed to the increase in stretching and shear deformation with increasing relative density, thereby increasing the tendency towards PYS. The finding of this work extends perspectives on PYS for the design of high-performance energy-absorbing lattice materials.

Post-yield softening of bending-dominated metal metamaterials

Post-yield softening (PYS) plays an important role in guiding the design of high-performance energy-absorbing lattice materials. PYS is usually restricted to lattice materials that are stretching-dominated according to the Gibson-Ashby model. Contrary to this long-held assumption, this work shows that PYS can also occur in various bending-dominated Ti-6Al-4V lattices with increasing relative density. The underlying mechanism for this unusual property is elucidated using the Timoshenko-beam theory. It is attributed to the increase in stretching and shear deformation with increasing relative density, thereby increasing the tendency towards PYS. The finding of this work extends perspectives on PYS for the design of high-performance energy-absorbing lattice materials.

Machine learning determination of applied behavioral analysis treatment plan type

BACKGROUND

Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment.

METHODS

Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment.

CONCLUSION

This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.

Outbreak of Ichthyophthirius multifiliis associated with Aeromonas hydrophila in Pangasianodon hypophthalmus: The role of turmeric oil in enhancing immunity and inducing resistance against co-infection

Ichthyophthirius multifiliis, a ciliated parasite causing ichthyophthiriasis (white spot disease) in freshwater fishes, results in significant economic loss to the aquaculture sector. One of the important predisposing factors for ichthyophthiriasis is low water temperature (i.e., below 20°C), which affects the health and makes freshwater fishes more susceptible to parasitic infections. During ichthyophthiriasis, fishes are stressed and acute immune reactions are compromised, which enables the aquatic bacterial pathogens to simultaneously infect the host and increase the severity of disease. In the present work, we aimed to understand the parasite–bacteria co-infection mechanism in fish. Later, Curcuma longa (turmeric) essential oil was used as a promising management strategy to improve immunity and control co-infections in fish. A natural outbreak of I. multifiliis was reported (validated by 16S rRNA PCR and sequencing method) in Pangasianodon hypophthalmus from a culture facility of ICAR-CIFRI, India. The fish showed clinical signs including hemorrhage, ulcer, discoloration, and redness in the body surface. Further microbiological analysis revealed that Aeromonas hydrophila was associated (validated by 16S rRNA PCR and sequencing method) with the infection and mortality of P. hypophthalmus, confirmed by hemolysin and survival assay. This created a scenario of co-infections, where both infectious agents are active together, causing ichthyophthiriasis and motile Aeromonas septicemia (MAS) in P. hypophthalmus. Interestingly, turmeric oil supplementation induced protective immunity in P. hypophthalmus against the co-infection condition. The study showed that P. hypophthalmus fingerlings supplemented with turmeric oil, at an optimum concentration (10 ppm), exhibited significantly increased survival against co-infection. The optimum concentration induced anti-stress and antioxidative response in fingerlings, marked by a significant decrease in cortisol and elevated levels of superoxide dismutase (SOD) and catalase (CAT) in treated animals as compared with the controls. Furthermore, the study indicated that supplementation of turmeric oil increases both non-specific and specific immune response, and significantly higher values of immune genes (interleukin-1β, transferrin, and C3), HSP70, HSP90, and IgM were observed in P. hypophthalmus treatment groups. Our findings suggest that C. longa (turmeric) oil modulates stress, antioxidant, and immunological responses, probably contributing to enhanced protection in P. hypophthalmus. Hence, the application of turmeric oil treatment in aquaculture might become a management strategy to control co-infections in fishes. However, this hypothesis needs further validation.

Impact of site of occlusion in proximal splenic artery embolisation for blunt splenic trauma

Background

Proximal splenic artery embolisation (PSAE) can be performed in stable patients with Association for the Surgery of Trauma (AAST) grade III-V splenic injury. PSAE reduces splenic perfusion but maintains viability of the spleen and pancreas via the collateral circulation. The hypothesized ideal location is between the dorsal pancreatic artery (DPA) and great pancreatic artery (GPA). This study compares the outcomes resulting from PSAE embolisation in different locations along the splenic artery.

Materials and methods

Retrospective review was performed of PSAE for blunt splenic trauma (2015–2020). Embolisation locations were divided into: Type I, proximal to DPA; Type II, DPA-GPA; Type III, distal to GPA. Fifty-eight patients underwent 59 PSAE: Type I (7); Type II (27); Type III (25). Data was collected on technical and clinical success, post-embolisation pancreatitis and splenic perfusion. Statistical significance was assessed using a chi-squared test.

Results

Technical success was achieved in 100% of cases. Clinical success was 100% for Type I/II embolisation and 88% for Type III: one patient underwent reintervention and two had splenectomies for ongoing instability. Clinical success was significantly higher in Type II embolisation compared to Type III (p = 0.02). No episodes of pancreatitis occurred post-embolisation. Where post-procedural imaging was obtained, splenic perfusion remained 100% in Type I and II embolisation and 94% in Type III. Splenic perfusion was significantly higher in the theorized ideal Type II group compared to Type I and III combined (p = 0.01).

Conclusion

The results support the proposed optimal embolisation location as being between the DPA and GPA.

Early prediction of central line associated bloodstream infection using machine learning

BACKGROUND

Central line-associated bloodstream infections (CLABSIs) are associated with significant morbidity, mortality, and increased healthcare costs. Despite the high prevalence of CLABSIs in the U.S., there are currently no tools to stratify a patient's risk of developing an infection as the result of central line placement. To this end, we have developed and validated a machine learning algorithm (MLA) that can predict a patient's likelihood of developing CLABSI using only electronic health record data in order to provide clinical decision support.

METHODS

We created three machine learning models to retrospectively analyze electronic health record data from 27,619 patient encounters. The models were trained and validated using an 80:20 split for the train and test data. Patients designated as having a central line procedure based on International Statistical Classification of Diseases and Related Health Problems 10 codes were included.

RESULTS

XGBoost was the highest performing MLA out of the three models, obtaining an AUROC of 0.762 for CLABSI risk prediction at 48 hours after the recorded time for central line placement.

CONCLUSIONS

Our results demonstrate that MLAs may be effective clinical decision support tools for assessment of CLABSI risk and should be explored further for this purpose.

Knowledge, Attitude and Level of Involvement of Married Males in Family Planning

Background There is an age-old notion that family planning is women's responsibility disregarding the fact that men have equal responsibility in fertility regulation. Although male involvement is getting more recognition, studies on men's role in family planning are very few in the number in this part of the world. Objective To assess the knowledge, attitude and level of male involvement in family planning and to find out the factors associated with male involvement by contraceptive usage. Method A community based cross-sectional study was done from May to July 2021 among 165 currently married male, who had at least one child, living in Singur district of West Bengal. Cluster sampling method was done to select study participants and data were collected by pre-designed pretested questionnaire. Descriptive statistics, multivariable logistic regression was applied and data were analysed applying SPSS software. Result Only 36.4% participants were directly involved in family planning either by using condom or by withdrawal method but 65.5% participants were indirectly involved in family planning through spousal communication either by approving contraceptive use to their spouse or by decision making regarding family planning. Moreover, barrier of contraceptives usage were side effect (27%) and fear of impotence (25.5%). Male involvement was significantly associated with participant's education [AOR (95% CI= 3.63 (1.45-9.05)], caste [AOR (95% CI= 7.06 (2.55-19.51)], number of living children [AOR (95%CI= 5.01(1.95-12.87)], desire for more child [AOR (95% CI=0.34 (.13-.87)] and attitude on family planning [AOR (95% CI= 3.55 (1.41-8.94)]. Conclusion This study identified the prevailing gender norms in rural areas. Advocacy for male involvement in family planning by health personnel during counselling of eligible couples should help in increasing contraceptive coverage in the long run.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

Background

Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.

Methods

A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.

Results

After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.

Conclusion

MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

Predicting pulmonary embolism among hospitalized patients with machine learning algorithms

Background

Objective

Materials and Methods

Results

Conclusions

Study of Λn FSI with Λ quasi-free productions on the 3H(e, e′K+)X reaction at JLab

Abstract. An nnΛ is a neutral baryon system with no charge. The study of the pure Λ-neutron system such as nnΛ gives us information on the Λn interaction. The nnΛ search experiment (E12-17-003) was performed at JLab Hall A in 2018. In this article, the Λn FSI was investigated by a shape analysis of the 3H(e, e′K+)X missing mass spectrum, and a preliminary result for the Λn FSI study is given.

Cross-section measurement of virtual photoproduction of iso-triplet three-body hypernucleus, Λnn

Missing-mass spectroscopy with the 3H(e, e′K+) reaction was carried out at Jefferson Lab’s (JLab) Hall A in Oct–Nov, 2018. The differential cross section for the 3H(γ∗, K+)Λnn was deduced at ω = Ee − Ee′ = 2.102 GeV and at the forward K+-scattering angle (0° ≤ θγ∗K ≤ 5°) in the laboratory frame. Given typical predicted energies and decay widths, which are (BΛ, Γ) = (−0.25, 0.8) and (−0.55, 4.7) MeV, the cross sections were found to be 11.2 ± 4.8(stat.)+4.1−2.1(sys.) and 18.1 ± 6.8(stat.)+4.2−2.9(sys.) nb/sr, respectively. The obtained result would impose a constraint for interaction models particularly between Λ and neutron by comparing to theoretical calculations.

Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus

INTRODUCTION

Diabetic kidney disease (DKD) accounts for the majority of increased risk of mortality for patients with diabetes, and eventually manifests in approximately half of those patients diagnosed with type 2 diabetes mellitus (T2DM). Although increased screening frequency can avoid delayed diagnoses, this is not uniformly implemented. The purpose of this study was to develop and retrospectively validate a machine learning algorithm (MLA) that predicts stages of DKD within 5 years upon diagnosis of T2DM.

RESEARCH DESIGN AND METHODS

Two MLAs were trained to predict stages of DKD severity, and compared with the Centers for Disease Control and Prevention (CDC) risk score to evaluate performance. The models were validated on a hold-out test set as well as an external dataset sourced from separate facilities.

RESULTS

The MLAs outperformed the CDC risk score in both the hold-out test and external datasets. Our algorithms achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 on the hold-out set for prediction of any-stage DKD and an AUROC of over 0.82 for more severe endpoints, compared with the CDC risk score with an AUROC <0.70 on all test sets and endpoints.

CONCLUSION

This retrospective study shows that an MLA can provide timely predictions of DKD among patients with recently diagnosed T2DM.

Early prediction of severe acute pancreatitis using machine learning

BACKGROUND

Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Risk stratification to identify SAP cases needing inpatient treatment is an important aspect of AP diagnosis.

METHODS

We developed machine learning algorithms to predict which patients presenting with AP would require treatment for SAP. Three models were developed using logistic regression, neural networks, and XGBoost. Models were assessed in terms of area under the receiver operating characteristic (AUROC) and compared to the Harmless Acute Pancreatitis Score (HAPS) and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores for AP risk stratification.

RESULTS

61,894 patients were used to train and test the machine learning models. With an AUROC value of 0.921, the model developed using XGBoost outperformed the logistic regression and neural network-based models. The XGBoost model also achieved a higher AUROC than both HAPS and BISAP for identifying patients who would be diagnosed with SAP.

CONCLUSIONS

Machine learning may be able to improve the accuracy of AP risk stratification methods and allow for more timely treatment and initiation of interventions.

Study of the Λ/Σ0 electroproduction in the low-Q2 region at JLab

We performed an experiment using tritium and hydrogen cryogenic gas targets at Thomas Jefferson National Accelerator Facility (JLab) in 2018 (E12-17-003)[1, 2]. In this article, we discuss the Λ/Σ0 hyperon electroproduction from hydrogen target. Elementary Λ/Σ0 hyperon production processes are important not only for an absolute mass scale calibration in our experiment, but also for the study of the electroproduction mechanisms themselves. In this article, we reported the results of the differential cross section for the p(e, e’K+)Λ/Σ0 reaction at Q2 ∼ 0.5 (GeV/c)2.

Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint)

Background

Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities.

Objective

The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care.

Methods

This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models.

Results

The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone.

Conclusions

This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.

How particle-particle and liquid-particle interactions govern the fate of evaporating liquid marbles

Liquid marbles refer to droplets that are covered with a layer of non-wetting particles. They are observed in nature and have practical significance. These squishy objects bounce, coalesce, break, inflate, and deflate while the liquid does not touch the substrate underneath. Despite the considerable cross-disciplinary interest and value of the research on liquid marbles, a unified framework for describing the mechanics of deflating liquid marbles-as the liquid evaporates-is unavailable. For instance, analytical approaches for modeling the evaporation of liquid marbles exploit empirical parameters that are not based on liquid-particle and particle-particle interactions. Here, we have combined complementary experiments and theory to fill this gap. To unentangle the contributions of particle size, roughness, friction, and chemical make-up, we investigated the evaporation of liquid marbles formed with particles of sizes varying over 7 nm-300 μm and chemical compositions ranging from hydrophilic to superhydrophobic. We demonstrate that the potential final states of evaporating liquid marbles are characterized by one of the following: (I) constant surface area, (II) particle ejection, or (III) multilayering. Based on these insights, we developed an evaporation model for liquid marbles that takes into account their time-dependent shape evolution. The model fits are in excellent agreement with our experimental results. Furthermore, this model and the general framework can provide mechanistic insights into extant literature on the evaporation of liquid marbles. Altogether, these findings advance our fundamental understanding of liquid marbles and should contribute to the rational development of technologies.

Personalized stratification of hospitalization risk amidst COVID-19: A machine learning approach

Objective: In the wake of COVID-19, the United States (U.S.) developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans deemed to be at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness, and may therefore be of limited use in decisions surrounding resource allocation to vulnerable populations. The objective of this study was to evaluate a machine learning algorithm for prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. Methods: The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S. policy-based criteria: age over 65; having a serious underlying health condition; age over 65 or having a serious underlying health condition; and age over 65 and having a serious underlying health condition. Results: This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus 62% identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. Conclusions: This algorithm may identify individuals likely to require hospitalization should they contract COVID-19. This information may be useful to guide vaccine distribution, anticipate hospital resource needs, and assist health care policymakers to make care decisions in a more principled manner.

Semi-supervised deep learning from time series clinical data for acute respiratory distress syndrome prediction: model development and validation study

Background

A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS).

Objective

In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data.

Methods

SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient’s peripheral oxygen saturation level fell below the normal range (<97%) to predict the subsequent development of ARDS during the remaining duration of patients’ hospital stay. Model performance was assessed with the area under the receiver operating characteristic curve and area under the precision recall curve of an external hold-out test set.

Results

For the whole data set, the median time between the first peripheral oxygen saturation measurement of <97% and subsequent respiratory failure was 21 hours. The area under the receiver operating characteristic curve for predicting subsequent ARDS development was 0.73 when the model was trained on a labeled data set of 6930 patients, 0.78 when the model was trained on the labeled data set that had been augmented with the unlabeled data set of 16,173 patients by using SSL techniques, and 0.84 when the model was trained on the entire training set of 23,103 labeled patients.

Conclusions

In the context of using time-series inpatient data and a careful model training design, unlabeled data can be used to improve the performance of machine learning models when labeled data for predicting ARDS development are scarce or expensive.

Predicting ventilator-associated pneumonia with machine learning

Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.

A machine learning approach to predicting risk of myelodysplastic syndrome

BACKGROUND

Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease.

METHODS

Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC).

RESULTS

On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively.

CONCLUSIONS

Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study

BACKGROUND

Despite the limitations in the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks.

OBJECTIVE

We aimed to conduct an exploratory analysis of potential correlations between the population distribution of cycle threshold (CT) values and COVID-19 dynamics, which were operationalized as percent positivity, transmission rate (Rt), and COVID-19 hospitalization count.

METHODS

In total, 148,410 specimens collected between September 15, 2020, and January 11, 2021, from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. The daily median CT value, daily Rt, daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression were used to evaluate possible associations between daily median CT values and outbreak measures. Cross-correlation plots were used to determine whether a time delay existed between changes in daily median CT values and measures of community disease dynamics.

RESULTS

Daily median CT values negatively correlated with the daily Rt values (P<.001), the daily COVID-19 hospitalization counts (with a 33-day time delay; P<.001), and the daily changes in percent positivity among testing samples (P<.001). Despite visual trends suggesting time delays in the plots for median CT values and outbreak measures, a statistically significant delay was only detected between changes in median CT values and COVID-19 hospitalization counts (P<.001).

CONCLUSIONS

This study adds to the literature by analyzing samples collected from an entire geographical area and contextualizing the results with other research investigating population CT values.

Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Background

Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

Results

When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO₂, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.

Conclusions

A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00255-w.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.

Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.

Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).

Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

BACKGROUND

The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments.

METHODS

Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined.

RESULTS

Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events.

CONCLUSION

Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Investigation of groundwater and soil quality near to a municipal waste disposal site in Silchar, Assam, India

Unscientific management of municipal solid waste is one of the direct sources of contamination in developing countries, such as India. The present investigation carried out during Oct–Dec 2019 attempts to assess the parameters, such as quality of groundwater and soil along three depths (0–5, 5–15 and 15–30 cm), in proximity to a dumping site in Silchar, a rapidly evolving city of North-East India. Standard protocols of soil and water quality assessments were carried out. The pH values of the surface soils were found to be slightly acidic. Decrease in acidity with increasing depth was observed in the study site. The relative abundance of the analyzed elements at all soil depths was Zn > Fe > Ni > Cu > Cr. Weak correlation between the concentration of Cu, Fe and Zn, and the bulk density of the soil highlighted the micronutrient status of the soil. The impact of the nearby dumpsite on trace element contamination is indicated by the ‘extremely contaminated’ status of the soils with respect to geo-accumulation index. Majority of the groundwater samples exhibited pH levels below the desired limits, making it unfit for consumption by local communities. While Fe, Cu and Ni levels in groundwater samples exceeded the guideline values, Cr and Zn concentrations were found to be within limits except one sample. Principal Component Analysis of the observed data was carried out to ascertain the predominant sources of contamination. The observations indicate the negative impacts of nearby dumpsite on environmental parameters, such as groundwater and soil quality, as highlighted in this research.

Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction

Introduction

Acute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.

Methods

A CNN prediction system was developed to use EHR data gathered during patients’ stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.

Results

On a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.

Conclusion

A CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient's risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.

Severe epidermolysis bullosa/Kindler syndrome-like phenotype of an autoinflammatory syndrome in a child

A 5-year-old boy presented with generalized cutaneous erosions, severe scarring, depigmentation and contractures affecting major joints. The lesions had initially affected his ears, nose, feet, and the genital and ocular mucosa, leading to significant depigmentation, scarring, contractures and mutilation. The whole of the trunk and limbs were involved at the time of presentation, with the exception of some islands of spared skin on the proximal thighs, legs, nipples and external genitalia. Electron microscopy revealed a split in the sublamina densa with the absence of anchoring fibrils, suggestive of dystrophic epidermolysis bullosa (EB). Immunofluorescence antigen mapping demonstrated a broad reticulate pattern of staining with collagen IV, VII, and laminin 332 in the floor of the blister, suggestive of Kindler syndrome. Next-generation sequencing revealed a de novo heterozygous missense mutation (a variant of unknown significance) in exon 22 of the phospholipase-C gamma 2 gene (PLCG2), which resulted in a substitution of serine by asparagine at codon 798 (p.Asp798Ser), a result that was validated using Sanger sequencing. The child was diagnosed with PLCG2-associated antibody deficiency and immune dysregulation (PLAID)/autoinflammation and PLCG2-associated antibody deficiency and immune dysregulation (APLAID) syndrome. The cutaneous and corneal erosions, inflammation and scarring of this magnitude, and the eventual result of death have not been described previously for the PLAID/APLAID spectrum previously. In conclusion, this was an unusual acquired autoinflammatory severe EB-like disease that may be associated with de novo PLCG2 mutation.