Categories
Nevin Manimala Statistics

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study

Ann Intern Med. 2023 Oct 10. doi: 10.7326/M23-0949. Online ahead of print.

ABSTRACT

BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models.

OBJECTIVE: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented.

DESIGN: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation.

SETTING: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts).

PATIENTS: 130 000 critical care admissions across both health systems.

INTERVENTION: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness.

MEASUREMENTS: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures.

RESULTS: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%.

LIMITATIONS: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated.

CONCLUSION: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling.

PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences.

PMID:37812781 | DOI:10.7326/M23-0949

Categories
Nevin Manimala Statistics

Would You Screen This Patient for Cognitive Impairment? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center

Ann Intern Med. 2023 Oct 10. doi: 10.7326/M23-1808. Online ahead of print.

ABSTRACT

Dementia, according to the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, is defined by a significant decline in 1 or more cognitive domains that interferes with a person’s independence in daily activities. Mild cognitive impairment (MCI) differs from dementia in that the impairment is not sufficient to interfere with independence. For the purposes of this discussion, cognitive impairment (CI) includes both dementia and MCI. Various screening tests are available for CI. These tests ask patients to perform a series of tasks that assess 1 or more domains of cognitive function or ask a caregiver to report on the patient’s abilities. A positive result on a screening test does not equate to a diagnosis of CI; rather, it should lead to additional testing to confirm the diagnosis. On review of the evidence, the U.S. Preventive Services Task Force (USPSTF) concluded in 2020 that the evidence was insufficient to assess the balance of benefits and harms of screening for CI in older adults (“I statement”). The USPSTF did clarify that although there is insufficient evidence, there may be important reasons to identify CI. In this article, 2 experts review the available evidence to answer the following questions: What screening tools are available, and how effective are they in identifying patients with CI? What interventions are available for patients found to have CI, to what extent do they improve patient outcomes, and what, if any, negative effects occur? And, would they recommend screening for CI, and why or why not?

PMID:37812780 | DOI:10.7326/M23-1808

Categories
Nevin Manimala Statistics

Risk for Congenital Anomalies in Children Conceived With Medically Assisted Fertility Treatment : A Population-Based Cohort Study

Ann Intern Med. 2023 Oct 10. doi: 10.7326/M23-0872. Online ahead of print.

ABSTRACT

BACKGROUND: More than 2 million children are conceived annually using assisted reproductive technologies (ARTs), with a similar number conceived using ovulation induction and intrauterine insemination (OI/IUI). Previous studies suggest that ART-conceived children are at increased risk for congenital anomalies (CAs). However, the role of underlying infertility in this risk remains unclear, and ART clinical and laboratory practices have changed drastically over time, particularly there has been an increase in intracytoplasmic sperm injection (ICSI) and cryopreservation.

OBJECTIVE: To investigate the role of underlying infertility and fertility treatment on CA risks in the first 2 years of life.

DESIGN: Propensity score-weighted population-based cohort study.

SETTING: New South Wales, Australia.

PARTICIPANTS: 851 984 infants (828 099 singletons and 23 885 plural children) delivered between 2009 and 2017.

MEASUREMENTS: Adjusted risk difference (aRD) in CAs of infants conceived through fertility treatment compared with 2 naturally conceived (NC) control groups-those with and without a parental history of infertility (NC-infertile and NC-fertile).

RESULTS: The overall incidence of CAs was 459 per 10 000 singleton births and 757 per 10 000 plural births. Compared with NC-fertile singleton control infants (n = 747 018), ART-conceived singleton infants (n = 31 256) had an elevated risk for major genitourinary abnormalities (aRD, 19.0 cases per 10 000 births [95% CI, 2.3 to 35.6]); the risk remained unchanged (aRD, 22 cases per 10 000 births [CI, 4.6 to 39.4]) when compared with NC-infertile singleton control infants (n = 36 251) (that is, after accounting for parental infertility), indicating that ART remained an independent risk. After accounting for parental infertility, ICSI in couples without male infertility was associated with an increased risk for major genitourinary abnormalities (aRD, 47.8 cases per 10 000 singleton births [CI, 12.6 to 83.1]). There was some suggestion of increased risk for CAs after fresh embryo transfer, although estimates were imprecise and inconsistent. There were no increased risks for CAs among OI/IUI-conceived infants (n = 13 574).

LIMITATIONS: This study measured the risk for CAs only in those children who were born at or after 20 weeks’ gestation. Observational study design precludes causal inference. Many estimates were imprecise.

CONCLUSION: Patients should be counseled on the small increased risk for genitourinary abnormalities after ART, particularly after ICSI, which should be avoided in couples without problems of male infertility.

PRIMARY FUNDING SOURCE: Australian National Health and Medical Research Council.

PMID:37812776 | DOI:10.7326/M23-0872

Categories
Nevin Manimala Statistics

Impact of Standardized Multidisciplinary Critical Care Training on Confidence with Critical Illness and Attitudes Towards Interprofessional Education and Multidisciplinary Care

J Intensive Care Med. 2023 Oct 9:8850666231201528. doi: 10.1177/08850666231201528. Online ahead of print.

ABSTRACT

INTRODUCTION: The Fundamental Critical Care Support Course (FCCS) is a standardized multidisciplinary program designed to educate participants on the basics of identification and management of patients with critical illness. Our objective was to evaluate the effect of FCCS participation on confidence in the assessment and management of critically ill patients and attitudes towards multidisciplinary education and interprofessional care in a multidisciplinary group of participants.

METHODS: Participants enrolled in the FCCS course from May 2018 to November 2019 were solicited to participate in a series of surveys evaluating their course experience and confidence in critical care. Attitudes towards multidisciplinary education and interprofessional care were evaluated using the Student Perceptions of Interprofessional Clinical Education-Revised Instrument version 2 (SPICE-R2) tool. A prospective pre- and post-design with a self-report survey including retrospective pre-training assessment and a 3-month follow-up was conducted. Statistical analysis was performed using descriptive statics and non-parametric methods.

RESULTS: 321 (97.9%) of the course participants enrolled in the study and completed the confidence survey and SPICE-R2 tool pre-course. Nurses (113, 35.4%) and physicians (110, 34.4%) made up the largest groups of participants, although physician assistants and paramedics were also well represented. Confidence in recognition and management of critical illness significantly improved across all studied domains after course completion, with the mean total confidence score improving from 32.96 pre-course to 41.10 post-course, P < 0.001. Attitudes towards multidisciplinary education and interprofessional care also improved (mean score 41.37 pre-course vs 42.71 post-course, P < 0.001), although pre-course numbers were higher than expected which limited the significance to only certain domains.

DISCUSSION: In a multidisciplinary group, completion of FCCS training led to increased confidence in all aspects of critical illness measured. A modest increase in attitudes regarding multidisciplinary education and interprofessional care was also demonstrated. Further study is needed to assess whether this increased confidence translates to improvements in patient care and outcomes.

PMID:37812739 | DOI:10.1177/08850666231201528

Categories
Nevin Manimala Statistics

Multilevel structural equation modeling of willingness-to-pay for fatality risk reduction: perspectives of driver and district levels

Int J Inj Contr Saf Promot. 2023 Oct 9:1-15. doi: 10.1080/17457300.2023.2266841. Online ahead of print.

ABSTRACT

Road accidents remain a serious problem and directly affect drivers. Therefore, the perspectives of drivers are important in improving road safety. The objectives of this study are to empirically examine damage due to road accidents using the willingness-to-pay (WTP) approach and to analyze the factors that influence WTP at the driver and district levels. This study obtained data on WTP derived from car drivers across Thailand, which covers 96 districts. The value of statistical life was 824,344 USD per fatality (2,296 million USD annually). The results of Multilevel Structural Equation Modeling revealed a statistically important insight. At the driver level, the Health Belief Model and sociodemographic exert influence on the intention to pay. The demographic factor that has the greatest influence on perceived risk and leads to a high intention to pay is the working age group (γ = 0.826). However, when considering the HBM, perceived susceptibility (γ = 0.901) emerges as the most valuable factor influencing drivers’ concerns about road accidents. On the other hand, district-level factors have a negative influence on the intention to pay for road safety measures. Among these factors, the law enforcement (γ = -0.555) practices implemented by local authorities have the most significant impact on drivers’ perspectives and intentions regarding WTP. This finding can be used as a guideline for budget allocation and policy recommendation for policymakers in improving road safety according to the area contexts.

PMID:37812734 | DOI:10.1080/17457300.2023.2266841

Categories
Nevin Manimala Statistics

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice

ACS Nano. 2023 Oct 9. doi: 10.1021/acsnano.3c04037. Online ahead of print.

ABSTRACT

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of “Nano-Tumor Database”, which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded “Nano-Tumor Database” and several statistical models that may help nanomedicine design in the future.

PMID:37812732 | DOI:10.1021/acsnano.3c04037

Categories
Nevin Manimala Statistics

Predicting the attention of others

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2307584120. doi: 10.1073/pnas.2307584120. Epub 2023 Oct 9.

ABSTRACT

As social animals, people are highly sensitive to the attention of others. Seeing someone else gaze at an object automatically draws one’s own attention to that object. Monitoring the attention of others aids in reconstructing their emotions, beliefs, and intentions and may play a crucial role in social alignment. Recently, however, it has been suggested that the human brain constructs a predictive model of other people’s attention that is far more involved than a moment-by-moment monitoring of gaze direction. The hypothesized model learns the statistical patterns in other people’s attention and extrapolates how attention is likely to move. Here, we tested the hypothesis of a predictive model of attention. Subjects saw movies of attention displayed as a bright spot shifting around a scene. Subjects were able to correctly distinguish natural attention sequences (based on eye tracking of prior participants) from altered sequences (e.g., played backward or in a scrambled order). Even when the attention spot moved around a blank background, subjects could distinguish natural from scrambled sequences, suggesting a sensitivity to the spatial-temporal statistics of attention. Subjects also showed an ability to recognize the attention patterns of different individuals. These results suggest that people possess a sophisticated model of the normal statistics of attention and can identify deviations from the model. Monitoring attention is therefore more than simply registering where someone else’s eyes are pointing. It involves predictive modeling, which may contribute to our remarkable social ability to predict the mind states and behavior of others.

PMID:37812722 | DOI:10.1073/pnas.2307584120

Categories
Nevin Manimala Statistics

The metabolomic physics of complex diseases

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2308496120. doi: 10.1073/pnas.2308496120. Epub 2023 Oct 9.

ABSTRACT

Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.

PMID:37812720 | DOI:10.1073/pnas.2308496120

Categories
Nevin Manimala Statistics

Identifying microscopic factors that influence ductility in disordered solids

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2307552120. doi: 10.1073/pnas.2307552120. Epub 2023 Oct 9.

ABSTRACT

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

PMID:37812709 | DOI:10.1073/pnas.2307552120

Categories
Nevin Manimala Statistics

Space weather disrupts nocturnal bird migration

Proc Natl Acad Sci U S A. 2023 Oct 17;120(42):e2306317120. doi: 10.1073/pnas.2306317120. Epub 2023 Oct 9.

ABSTRACT

Space weather, including solar storms, can impact Earth by disturbing the geomagnetic field. Despite the known dependence of birds and other animals on geomagnetic cues for successful seasonal migrations, the potential effects of space weather on organisms that use Earth’s magnetic field for navigation have received little study. We tested whether space weather geomagnetic disturbances are associated with disruptions to bird migration at a macroecological scale. We leveraged long-term radar data to characterize the nightly migration dynamics of the nocturnally migrating North American avifauna over 22 y. We then used concurrent magnetometer data to develop a local magnetic disturbance index associated with each radar station (ΔBmax), facilitating spatiotemporally explicit analyses of the relationship between migration and geomagnetic disturbance. After controlling for effects of atmospheric weather and spatiotemporal patterns, we found a 9 to 17% decrease in migration intensity in both spring and fall during severe space weather events. During fall migration, we also found evidence for decreases in effort flying against the wind, which may represent a depression of active navigation such that birds drift more with the wind during geomagnetic disturbances. Effort flying against the wind in the fall was most reduced under both overcast conditions and high geomagnetic disturbance, suggesting that a combination of obscured celestial cues and magnetic disturbance may disrupt navigation. Collectively, our results provide evidence for community-wide avifaunal responses to geomagnetic disturbances driven by space weather during nocturnal migration.

PMID:37812699 | DOI:10.1073/pnas.2306317120