Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications

Importance: Postoperative complications can significantly impact perioperative care management and planning. Objectives: To assess machine learning (ML) models for predicting postoperative complications using independent and combined preoperative and intraoperative data and their clinically meaningful model-agnostic interpretations. Design, Setting, and Participants: This retrospective cohort study assessed 111 888 operations performed on adults at a single academic medical center from June 1, 2012, to August 31, 2016, with a mean duration of follow-up based on the length of postoperative hospital stay less than 7 days. Data analysis was performed from February 1 to September 31, 2020. Main Outcomes and Measures: Outcomes included 5 postoperative complications: acute kidney injury (AKI), delirium, deep vein thrombosis (DVT), pulmonary embolism (PE), and pneumonia. Patient and clinical characteristics available preoperatively, intraoperatively, and a combination of both were used as inputs for 5 candidate ML models: logistic regression, support vector machine, random forest, gradient boosting tree (GBT), and deep neural network (DNN). Model performance was compared using the area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using Shapley Additive Explanations by transforming model features into clinical variables and representing them as patient-specific visualizations. Results: A total of 111 888 patients (mean [SD] age, 54.4 [16.8] years; 56 915 [50.9%] female; 82 533 [73.8%] White) were included in this study. The best-performing model for each complication combined the preoperative and intraoperative data with the following AUROCs: pneumonia (GBT), 0.905 (95% CI, 0.903-0.907); AKI (GBT), 0.848 (95% CI, 0.846-0.851); DVT (GBT), 0.881 (95% CI, 0.878-0.884); PE (DNN), 0.831 (95% CI, 0.824-0.839); and delirium (GBT), 0.762 (95% CI, 0.759-0.765). Performance of models that used only preoperative data or only intraoperative data was marginally lower than that of models that used combined data. When adding variables with missing data as input, AUROCs increased from 0.588 to 0.905 for pneumonia, 0.579 to 0.848 for AKI, 0.574 to 0.881 for DVT, 0.5 to 0.831 for PE, and 0.6 to 0.762 for delirium. The Shapley Additive Explanations analysis generated model-agnostic interpretation that illustrated significant clinical contributors associated with risks of postoperative complications. Conclusions and Relevance: The ML models for predicting postoperative complications with model-agnostic interpretation offer opportunities for integrating risk predictions for clinical decision support. Such real-time clinical decision support can mitigate patient risks and help in anticipatory management for perioperative contingency planning

Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.Comment: Plos ONE paper. Previous version was withdrawn to be updated by the journal's pdf versio

Oligo-Miocene magnetostratigraphy and rock magnetism of the Xishuigou section, Subei (Gansu Province, western China) and implications for shallow inclinations in central Asia.

Magnetostratigraphy of 222 remanent directions together with late Oligocene to early Miocene mammal and charophyte paleontology suggest that 2179 m of the Xishuigou section (Subei, Gansu Province, China) were deposited from ~26 to ~19 Ma. Stratigraphic patterns of bulk susceptibility, anisotropy of magnetic susceptibility parameters, and natural and anhysteretic remanent magnetization intensities demonstrate that (1) faulting does not significantly affect the record, (2) sediment deposition was relatively continuous, (3) sediment source changed around 23 Ma, and (4) rapid uplift near Subei occurred at 21 Ma. Subei rotated 27° ± 5° counterclockwise with respect to the 20 Ma pole from the Eurasian synthetic apparent polar wander path. Folding and rotation of the section took place after 19 Ma. The paleolatitude of Subei is 14° less than at present and 19° ± 3° less than predicted from the reference pole. Both rock magnetic and paleomagnetic data sets suggest that the unusually low paleolatitude is the result of synsedimentary inclination shallowing, a phenomenon which has likely affected other paleomagnetic data from central Asia

A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models

This paper presents a cross-country comparison of significant predictors of small business failure between Italy and the UK. Financial measures of profitability, leverage, coverage, liquidity, scale and non-financial information are explored, some commonalities and differences are highlighted. Several models are considered, starting with the logistic regression which is a standard approach in credit risk modelling. Some important improvements are investigated. Generalised Extreme Value (GEV) regression is applied in contrast to the logistic regression in order to produce more conservative estimates of default probability. The assumption of non-linearity is relaxed through application of BGEVA, non-parametric additive model based on the GEV link function. Two methods of handling missing values are compared: multiple imputation and Weights of Evidence (WoE) transformation. The results suggest that the best predictive performance is obtained by BGEVA, thus implying the necessity of taking into account the low volume of defaults and non-linear patterns when modelling SME performance. WoE for the majority of models considered show better prediction as compared to multiple imputation, suggesting that missing values could be informative

Global Burden of Sickle Cell Anaemia in Children under Five, 2010-2050: Modelling Based on Demographics, Excess Mortality, and Interventions

The global burden of sickle cell anaemia (SCA) is set to rise as a consequence of improved survival in high-prevalence low- and middle-income countries and population migration to higher-income countries. The host of quantitative evidence documenting these changes has not been assembled at the global level. The purpose of this study is to estimate trends in the future number of newborns with SCA and the number of lives that could be saved in under-five children with SCA by the implementation of different levels of health interventions.First, we calculated projected numbers of newborns with SCA for each 5-y interval between 2010 and 2050 by combining estimates of national SCA frequencies with projected demographic data. We then accounted for under-five mortality (U5m) projections and tested different levels of excess mortality for children with SCA, reflecting the benefits of implementing specific health interventions for under-five patients in 2015, to assess the number of lives that could be saved with appropriate health care services. The estimated number of newborns with SCA globally will increase from 305,800 (confidence interval [CI]: 238,400-398,800) in 2010 to 404,200 (CI: 242,500-657,600) in 2050. It is likely that Nigeria (2010: 91,000 newborns with SCA [CI: 77,900-106,100]; 2050: 140,800 [CI: 95,500-200,600]) and the Democratic Republic of the Congo (2010: 39,700 [CI: 32,600-48,800]; 2050: 44,700 [CI: 27,100-70,500]) will remain the countries most in need of policies for the prevention and management of SCA. We predict a decrease in the annual number of newborns with SCA in India (2010: 44,400 [CI: 33,700-59,100]; 2050: 33,900 [CI: 15,900-64,700]). The implementation of basic health interventions (e.g., prenatal diagnosis, penicillin prophylaxis, and vaccination) for SCA in 2015, leading to significant reductions in excess mortality among under-five children with SCA, could, by 2050, prolong the lives of 5,302,900 [CI: 3,174,800-6,699,100] newborns with SCA. Similarly, large-scale universal screening could save the lives of up to 9,806,000 (CI: 6,745,800-14,232,700) newborns with SCA globally, 85% (CI: 81%-88%) of whom will be born in sub-Saharan Africa. The study findings are limited by the uncertainty in the estimates and the assumptions around mortality reductions associated with interventions.Our quantitative approach confirms that the global burden of SCA is increasing, and highlights the need to develop specific national policies for appropriate public health planning, particularly in low- and middle-income countries. Further empirical collaborative epidemiological studies are vital to assess current and future health care needs, especially in Nigeria, the Democratic Republic of the Congo, and India

The interaction between a sexually transferred steroid hormone and a female protein regulates oogenesis in the malaria mosquito anopheles gambiae

Molecular interactions between male and female factors during mating profoundly affect the reproductive behavior and physiology of female insects. In natural populations of the malaria mosquito Anopheles gambiae, blood-fed females direct nutritional resources towards oogenesis only when inseminated. Here we show that the mating-dependent pathway of egg development in these mosquitoes is regulated by the interaction between the steroid hormone 20-hydroxy-ecdysone (20E) transferred by males during copulation and a female Mating-Induced Stimulator of Oogenesis (MISO) protein. RNAi silencing of MISO abolishes the increase in oogenesis caused by mating in blood-fed females, causes a delay in oocyte development, and impairs the function of male-transferred 20E. Co-immunoprecipitation experiments show that MISO and 20E interact in the female reproductive tract. Moreover MISO expression after mating is induced by 20E via the Ecdysone Receptor, demonstrating a close cooperation between the two factors. Male-transferred 20E therefore acts as a mating signal that females translate into an increased investment in egg development via a MISO-dependent pathway. The identification of this male–female reproductive interaction offers novel opportunities for the control of mosquito populations that transmit malaria

Status of Muon Collider Research and Development and Future Plans

The status of the research on muon colliders is discussed and plans are outlined for future theoretical and experimental studies. Besides continued work on the parameters of a 3-4 and 0.5 TeV center-of-mass (CoM) energy collider, many studies are now concentrating on a machine near 0.1 TeV (CoM) that could be a factory for the s-channel production of Higgs particles. We discuss the research on the various components in such muon colliders, starting from the proton accelerator needed to generate pions from a heavy-Z target and proceeding through the phase rotation and decay ($\pi \to \mu \nu_{\mu}$) channel, muon cooling, acceleration, storage in a collider ring and the collider detector. We also present theoretical and experimental R & D plans for the next several years that should lead to a better understanding of the design and feasibility issues for all of the components. This report is an update of the progress on the R & D since the Feasibility Study of Muon Colliders presented at the Snowmass'96 Workshop [R. B. Palmer, A. Sessler and A. Tollestrup, Proceedings of the 1996 DPF/DPB Summer Study on High-Energy Physics (Stanford Linear Accelerator Center, Menlo Park, CA, 1997)].Comment: 95 pages, 75 figures. Submitted to Physical Review Special Topics, Accelerators and Beam

Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base

Quantum state preparation and macroscopic entanglement in gravitational-wave detectors

Long-baseline laser-interferometer gravitational-wave detectors are operating at a factor of 10 (in amplitude) above the standard quantum limit (SQL) within a broad frequency band. Such a low classical noise budget has already allowed the creation of a controlled 2.7 kg macroscopic oscillator with an effective eigenfrequency of 150 Hz and an occupation number of 200. This result, along with the prospect for further improvements, heralds the new possibility of experimentally probing macroscopic quantum mechanics (MQM) - quantum mechanical behavior of objects in the realm of everyday experience - using gravitational-wave detectors. In this paper, we provide the mathematical foundation for the first step of a MQM experiment: the preparation of a macroscopic test mass into a nearly minimum-Heisenberg-limited Gaussian quantum state, which is possible if the interferometer's classical noise beats the SQL in a broad frequency band. Our formalism, based on Wiener filtering, allows a straightforward conversion from the classical noise budget of a laser interferometer, in terms of noise spectra, into the strategy for quantum state preparation, and the quality of the prepared state. Using this formalism, we consider how Gaussian entanglement can be built among two macroscopic test masses, and the performance of the planned Advanced LIGO interferometers in quantum-state preparation

Implications For The Origin Of GRB 051103 From LIGO Observations

We present the results of a LIGO search for gravitational waves (GWs) associated with GRB 051103, a short-duration hard-spectrum gamma-ray burst (GRB) whose electromagnetically determined sky position is coincident with the spiral galaxy M81, which is 3.6 Mpc from Earth. Possible progenitors for short-hard GRBs include compact object mergers and soft gamma repeater (SGR) giant flares. A merger progenitor would produce a characteristic GW signal that should be detectable at the distance of M81, while GW emission from an SGR is not expected to be detectable at that distance. We found no evidence of a GW signal associated with GRB 051103. Assuming weakly beamed gamma-ray emission with a jet semi-angle of 30 deg we exclude a binary neutron star merger in M81 as the progenitor with a confidence of 98%. Neutron star-black hole mergers are excluded with > 99% confidence. If the event occurred in M81 our findings support the the hypothesis that GRB 051103 was due to an SGR giant flare, making it the most distant extragalactic magnetar observed to date.Comment: 8 pages, 3 figures. For a repository of data used in the publication, go to: https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=15166 . Also see the announcement for this paper on ligo.org at: http://www.ligo.org/science/Publication-GRB051103/index.ph