Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.

Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. However, use of EHR data is hindered by a lack of direct event time observations. Occurrence times of relevant diagnostic codes or target disease mentions in clinical notes are at best a good approximation of the true disease onset time. On the other hand, extracting precise information on the exact event time requires laborious manual chart review and is sometimes altogether infeasible due to a lack of detailed documentation. Current status labels-binary indicators of phenotype status during follow-up-are significantly more efficient and feasible to compile, enabling more precise survival curve estimation given limited resources. Existing survival analysis methods using current status labels focus almost entirely on supervised estimation, and naive incorporation of unlabeled data into these methods may lead to biased estimates. In this article, we propose Semisupervised Calibration of Risk with Noisy Event Times (SCORNET), which yields a consistent and efficient survival function estimator by leveraging a small set of current status labels and a large set of informative features. In addition to providing theoretical justification of SCORNET, we demonstrate in both simulation and real-world EHR settings that SCORNET achieves efficiency akin to the parametric Weibull regression model, while also exhibiting semi-nonparametric flexibility and relatively low empirical bias in a variety of generative settings.

The current status of clinical trials on cancer and age disparities among the most common cancer trial participants.

OBJECTIVE: To illustrate the status of all cancer clinical trials and characterize clinical trial enrollment disparities in the most common cancer. METHODS: Clinical trial data were extracted from ClinicalTrials.gov website. All searched clinical trials were included in the current status analysis of clinical trials on cancer. Among all the clinical trials, only trials addressing single disease sites of breast, prostate, colorectal, or lung (BPCRL) cancer were included in the age disparities analysis. The difference in median age (DMA) between the trial participant median age and the population-based disease-site-specific median age was calculated for each trial. RESULTS: A total of 7747 clinical trials were included in the current status analysis of clinical trials on cancer. The number of registered trials had been increasing from 2008 to 2021 (AAPC = 50.60, 95% CI 36.60, 66.00, P < 0.05). Of the 7747 trials, 1.50% (116) of the studies were clinical trials for the elderly aged 60 years or older. 322 trials were included in the age disparities analysis. For all trials, the median DMA was - 8.15 years (P25, P75, - 10.83 to - 2.98 years, P < 0.001). The median DMA were - 9.55 years (P25, P75, - 11.63 to - 7.11 years), - 7.10 years (P25, P75, - 9.80 to - 5.70 years), - 9.75 years (P25, P75, - 11.93 to - 7.35 years), 3.50 years (P25, P75, 0.60 to 4.55 years), respectively, for breast cancer, colorectal cancer, lung cancer and prostate cancer. CONCLUSION: The numbers of registered clinical trials show an upward trend. Age disparities between trial participants and diagnosed disease population are present in BPCRL cancer trials and appear to be increasing over time. Equitable participation in clinical trials on the basis of age is crucial for advancing medical knowledge and evaluating the safety and efficacy of new treatments that are generalizable to aging populations.

Deep partially linear cox model for current status data.

Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained by using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is $\sqrt{n}$-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to real-world data on news popularity.

Martingale-residual-based greedy model averaging for high-dimensional current status data.

Current status data are a type of failure time data that arise when the failure time of study subject cannot be determined precisely but is known only to occur before or after a random monitoring time. Variable selection methods for the failure time data have been discussed extensively in the literature. However, the statistical inference of the model selected based on the variable selection method ignores the uncertainty caused by model selection. To enhance the prediction accuracy for risk quantities such as survival probability, we propose two optimal model averaging methods under semiparametric additive hazards models. Specifically, based on martingale residuals processes, a delete-one cross-validation (CV) process is defined, and two new CV functional criteria are derived for choosing model weights. Furthermore, we present a greedy algorithm for the implementation of the techniques, and the asymptotic optimality of the proposed model averaging approaches is established, along with the convergence of the greedy averaging algorithms. A series of simulation experiments demonstrate the effectiveness and superiority of the proposed methods. Finally, a real-data example is provided as an illustration.

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations. OBJECTIVE: We develop a Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) algorithm based on extensive unlabeled longitudinal Electronic Health Records (EHR) data augmented by a limited set of gold standard labels on the binary status information indicating whether the clinical event of interest occurred during the follow-up period. METHODS: The SeDDLeR algorithm calculates an individualized risk of developing future clinical events over time using each patient's baseline EHR features via the following steps: (1) construction of an initial EHR-derived surrogate as a proxy for the onset status; (2) deep learning calibration of the surrogate along gold-standard onset status; and (3) semi-supervised deep learning for risk prediction combining calibrated surrogates and gold-standard onset status. To account for missing onset time and heterogeneous follow-up, we introduce temporal kernel weighting. We devise a Gated Recurrent Units (GRUs) module to capture temporal characteristics. We subsequently assess our proposed SeDDLeR method in simulation studies and apply the method to the Massachusetts General Brigham (MGB) Biobank to predict type 2 diabetes (T2D) risk. RESULTS: SeDDLeR outperforms benchmark risk prediction methods, including Semi-parametric Transformation Model (STM) and DeepHit, with consistently best accuracy across experiments. SeDDLeR achieved the best C-statistics ( 0.815, SE 0.023; vs STM +.084, SE 0.030, P-value .004; vs DeepHit +.055, SE 0.027, P-value .024) and best average time-specific AUC (0.778, SE 0.022; vs STM + 0.059, SE 0.039, P-value .067; vs DeepHit + 0.168, SE 0.032, P-value <0.001) in the MGB T2D study. CONCLUSION: SeDDLeR can train robust risk prediction models in both real-world EHR and synthetic datasets with minimal requirements of labeling event times. It holds the potential to be incorporated for future clinical trial recruitment or clinical decision-making.

An in-depth analysis of COVID-19 treatment: Present situation and prospects.

Coronavirus disease 2019 (COVID-19) the most contagious infection caused by the unique type of coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), produced a global pandemic that wreaked havoc on the health-care system, resulting in high morbidity and mortality. Several methods were implemented to tackle the virus, including the repurposing of existing medications and the development of vaccinations. The purpose of this article is to provide a complete summary of the current state and future possibilities for COVID-19 therapies. We describe the many treatment classes, such as antivirals, immunomodulators, and monoclonal antibodies, that have been repurposed or developed to treat COVID-19. We also looked at the clinical evidence for these treatments, including findings from observational studies and randomized-controlled clinical trials, and highlighted the problems and limitations of the available evidence. Furthermore, we reviewed existing clinical trials and prospective COVID-19 therapeutic options, such as novel medication candidates and combination therapies. Finally, we discussed the long-term consequences of COVID-19 and the importance of ongoing research into the development of viable treatments. This review will help physicians, researchers, and policymakers to understand the prevention and mitigation of COVID-19.

Appraising Nigeria's approach to combating antimicrobial resistance.

AIM: Antibiotic consumption has increased dramatically in recent years, and this increase is predominantly fuelled by low- and middle-income countries. This is a worrying report, as antimicrobial resistance (AMR) is primarily driven by antibiotic consumption. To combat AMR, the Nigerian Ministry of Health established the Antimicrobial Resistance Technical Working Group (AMR-TWG), which developed and implemented the National Action Plan on Antimicrobial Resistance. This study aims to identify and appraise the current situation in the fight against AMR in Nigeria. METHODS: This study contains a qualitative descriptive assessment of the advancements and the present status of efforts to combat AMR in Nigeria following the execution of the 2017-2022 National Action Plan (NAP) for AMR. The data for this study is soured primarily from the official national policy document on AMR and the responses of the Tracking AMR Country Self-assessment Survey (TrACSS). RESULTS: The results from this study reveal that there have been significant efforts aimed at addressing AMR in Nigeria. These efforts have been focused on improving public awareness of AMR, improving One Health surveillance of AMR, improving infection prevention and control, improving antimicrobial stewardship practices in the country, and investing in research on AMR. However, significant gaps still exist in each of these focus areas that can potentially undermine the attempts that have been made hitherto. CONCLUSIONS: Nigeria's commitment to the fight against AMR, as exemplified by the 2017-2022 National Action Plan, needs to be sustained and reinforced to safeguard public health and promote responsible antimicrobial use.

Lessons from the COVID-19 pandemic.

The numbers of diagnosed and reported cases of infection with the SARS-CoV-2 virus causing the disease COVID-19, which grew into a global pandemic, have remained consistently low in all countries, including the Czech Republic, since May 2023, when the World Health Organization declared an end to the pandemic. However, it must be said that the measures implemented to control this infection did not meet all expectations. Although new mutations of the virus that can potentially cause disease, continue to emerge, it appears that most people have gradually learned to coexist with them. However, due to some unique properties of the SARS-CoV-2 virus and its variants, there will still be predisposed individuals who will develop illness and need hospitalization along with effective treatment to be supported and monitored by adequate laboratory tests. This article is a commentary on this issue and deals primarily with the diagnosis and care of early-phase COVID-19 patients. Author's translation of the article into English is available at: https://www.spadia.cz/media/2085/lessons fromthecovid-19pandemic.pdf.

Risk prediction with imperfect survival outcome information from electronic health records.

Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.

Two-phase designs with current status data.

We consider the design and analysis of two-phase studies aiming to assess the relation between a fixed (eg, genetic) marker and an event time under current status observation. We consider a common setting in which a phase I sample is comprised of a large cohort of individuals with outcome (ie, current status) data and a vector of inexpensive covariates. Stored biospecimens for individuals in the phase I sample can be assayed to record the marker of interest for individuals selected in a phase II sub-sample. The design challenge is then to select the phase II sub-sample in order to maximize the precision of the marker effect on the time of interest under a proportional hazards model. This problem has not been examined before for current status data and the role of the assessment time is highlighted. Inference based on likelihood and inverse probability weighted estimating functions are considered, with designs centered on score-based residuals, extreme current status observations, or stratified sampling schemes. Data from a registry of patients with psoriatic arthritis is used in an illustration where we study the risk of diabetes as a comorbidity.

Semiparametric probit regression model with misclassified current status data.

Current status data arise when each subject under study is examined only once at an observation time, and one only knows the failure status of the event of interest at the observation time rather than the exact failure time. Moreover, the obtained failure status is frequently subject to misclassification due to imperfect tests, yielding misclassified current status data. This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received considerable attention in failure time data analysis. We consider the nonparametric maximum likelihood estimation and develop an expectation-maximization (EM) algorithm by incorporating the generalized pool-adjacent-violators (PAV) algorithm to maximize the intractable likelihood function. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semiparametrically efficient. Furthermore, the numerical results in simulation studies indicate that the proposed method performs satisfactorily in finite samples and outperforms the naive method that ignores misclassification. We then apply the proposed method to a real dataset on chlamydia infection.

Maximum approximate likelihood estimation in accelerated failure time model for interval-censored data.

The approximate Bernstein polynomial model, a mixture of beta distributions, is applied to obtain maximum likelihood estimates of the regression coefficients, the baseline density and the survival functions in an accelerated failure time model based on interval censored data including current status data. The estimators of the regression coefficients and the underlying baseline density function are shown to be consistent with almost parametric rates of convergence under some conditions for uncensored and/or interval censored data. Simulation shows that the proposed method is better than its competitors. The proposed method is illustrated by fitting the Breast Cosmetic and the HIV infection time data using the accelerated failure time model.

Current status and future perspective of radiopharmaceuticals in China.

Radiopharmaceuticals are essential components of nuclear medicine and serve as one of the cornerstones of molecular imaging and precision medicine. They provide new means and approaches for early diagnosis and treatment of diseases. After decades of development and hard efforts, a relatively matured radiopharmaceutical production and management system has been established in China with high-quality facilities. This review provides an overview of the current status of radiopharmaceuticals on production and distribution, clinical application, and regulatory supervision and also describes some important advances in research and development and clinical translation of radiopharmaceuticals in the past 10 years. Moreover, some prospects of research and development of radiopharmaceuticals in the near future are discussed.

Self-reporting and screening: Data with right-censored, left-censored, and complete observations.

We consider survival data that combine three types of observations: uncensored, right-censored, and left-censored. Such data arises from screening a medical condition, in situations where self-detection arises naturally. Our goal is to estimate the failure-time distribution, based on these three observation types. We propose a novel methodology for distribution estimation using both semiparametric and nonparametric techniques. We then evaluate the performance of these estimators via simulated data. Finally, as a case study, we estimate the patience of patients who arrive at an emergency department and wait for treatment. Three categories of patients are observed: those who leave the system and announce it, and thus their patience time is observed; those who get service and thus their patience time is right-censored by the waiting time; and those who leave the system without announcing it. For this third category, the patients' absence is revealed only when they are called to service, which is after they have already left; formally, their patience time is left-censored. Other applications of our proposed methodology are discussed.

Inflammation: Biochemistry, cellular targets, anti-inflammatory agents and challenges with special emphasis on cyclooxygenase-2.

Keeping in view the involvement of inflammation in the pathogenesis of several diseases including cancer, diabetes, neurodegenerative disorders and rheumatoid arthritis, herein, we review the processes for the initiation of inflammation and the treatment measures. While focusing on the cyclooxygenase mediated arachidonic acid metabolic pathways, biochemistry of inflammatory prostaglandins is discussed. The data corresponding to efficacy, pharmacokinetic profile and the side effects of the available natural and synthetic anti-inflammatory drugs is reviewed. Moreover, the given information for the drug-based design of new anti-inflammatory agents may help in the development of more potent and safe molecules.

Korean Clinical Practice Guidelines: Current Status of Adherence to the RIGHT Checklist.

BACKGROUND: The Korean Academy of Medical Sciences (KAMS) has been utilizing AGREE II to audit the quality of clinical practice guidelines (CPGs) developed in Korea. Monitoring the RIGHT Checklist adherence could help monitor the quality status and discover areas for improvement of CPG development. METHODS: We included 129 CPGs from the past 5 years and assessed each item of the RIGHT Checklist. STATA version 15.0 was used for statistical analysis. RESULTS: Among the seven sections of the RIGHT checklist, sections with a full compliance rate over 60% were 'basic information' (65%) and 'background' (66%). The other sections' mean full compliance rates were 'Evidence' 52%, 'Recommendation' 35%, 'Review and quality assurance' 25% and 'Funding, declaration and management of interest' 17%. Sections with a partial compliance rate over 60% were 'Recommendation' (60%) and 'Funding, declaration and management of interest' (70%). Non-compliance was highest in the 'Review and quality assurance' (17%) domain. In comparison between groups 1 (under median group) and 2 (over median group), group 2 showed a tendency to have multi-stakeholder involvement and present sufficient information on financial resources and conflict of interest declarations. For the CPGs developmental methodology aspect, group 2 provided more pertinent information than group 1 about supporting evidence-making and the process from evidence to recommendation. CONCLUSION: This study evaluated adherence to the RIGHT Checklist of CPGs developed in Korea. It can provide helpful information to develop strategic plans for enhancing the capabilities of developing CPGs in Korea.

Current Status of Thalassemia in Lao People's Democratic Republic.

Thalassemia is a major public health and economical burden in Lao People's Democratic Republic (Lao PDR). This study is aiming to elaborate the current situation of Thalassemia in Laos. α- and ß-thalassemia (α- and ß-thal) includes the common Hb S (HBB: c.20A>T) and hemoglobins (Hbs) such as Hb Constant Spring (Hb CS or HBA2: c.427T>C) and Hb E (HBB: c.79G>A) that are prevalent in the country. Overall, the prevalence of α-thal in Lao PDR is 26.8%. There was high prevalence of homozygous (12.8%) and heterozygous (39.7%) Hb E among migrant workers from Lao PDR who crossed the border to work in Thailand. Iron chelation, blood transfusion, prenatal screening and diagnosis, comprehensive treatment are still the major problems. Splenectomy is still performed. A national registry has still not been established. This is a national economic burden for the country. Thalassemia prevention and control strategy should be established and advocated by the government in order to reduce morbidity and premature mortality.

Bias correction via outcome reassignment for cross-sectional data with binary disease outcome.

Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies to identify the relationship between covariates and disease outcome. A cross-sectional population is defined as a population of living individuals at the sampling or observational time. It is generally understood that binary disease outcome from cross-sectional data contains less information than longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. Wang and Yang (2021) presented the complexity and bias in cross-sectional data with binary disease outcome with detailed analytical explorations into the data structure. As the distribution of the cross-sectional binary outcome is quite different from the population risk distribution, bias can arise when using cross-sectional data analysis to draw inference for population risk. In this paper we argue that the commonly adopted age-specific risk probability is biased for the estimation of population risk and propose an outcome reassignment approach which reassigns a portion of the observed binary outcome, 0 or 1, to the other disease category. A sign test and a semiparametric pseudo-likelihood method are developed for analyzing cross-sectional data using the OR approach. Simulations and an analysis based on Alzheimer's Disease data are presented to illustrate the proposed methods.

[Current status and development strategy of ISO/TC 249 standards for Chinese materia medica].

The internationalization of traditional Chinese medicine(TCM) is one of the strategic development objectives in China, which has been incorporated into the national strategy as an important part of the Belt and Road Initiative development strategy. As the basis and prerequisite of TCM development, Chinese materia medica(CMM) has a direct impact on the internationalization of TCM. The International Organization for Standardization(ISO) is a global organization composed of national standardization bodies, and the ISO standards impact the world's economy, trade, communication and cooperation. Based on a brief introduction to ISO/Traditional Chinese Medicine Technical Committee(ISO/TC 249), this study elaborates the necessity of establishing ISO standards for CMM and analyzes the current status and challenges faced by the formulation of international standards for CMM. Finally, this study puts forward the development strategy of international standards for CMM. Specifically, efforts should be made to develop top-level design with international market demands as the guidance and improve the quality of standards to accelerate the transformation of domestic high-quality standards into international standards. Moreover, measures should be taken to give full play to the positive role of enterprises in the formulation of standards, vigorously cultivate compound talents for international standardization of TCM, and constantly strengthen international cooperation. The experience and thinking are of guiding significance for the scientific, efficient and reasonable formulation of high-quality ISO standards for CMM in the future.

Estimating menarcheal age distribution from partially recalled data.

In a cross-sectional study, adolescent and young adult females were asked to recall the time of menarche, if experienced. Some respondents recalled the date exactly, some recalled only the month or the year of the event, and some were unable to recall anything. We consider estimation of the menarcheal age distribution from this interval-censored data. A complicated interplay between age-at-event and calendar time, together with the evident fact of memory fading with time, makes the censoring informative. We propose a model where the probabilities of various types of recall would depend on the time since menarche. For parametric estimation, we model these probabilities using multinomial regression function. Establishing consistency and asymptotic normality of the parametric maximum likelihood estimator requires a bit of tweaking of the standard asymptotic theory, as the data format varies from case to case. We also provide a non-parametric maximum likelihood estimator, propose a computationally simpler approximation, and establish the consistency of both these estimators under mild conditions. We study the small sample performance of the parametric and non-parametric estimators through Monte Carlo simulations. Moreover, we provide a graphical check of the assumption of the multinomial model for the recall probabilities, which appears to hold for the menarcheal data set. Our analysis shows that the use of the partially recalled part of the data indeed leads to smaller confidence intervals of the survival function.