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Early Diagnosis

Early diagnosis refers to the identification of diseases or conditions at an early stage, before the onset of overt symptoms.
This can lead to more effective treatment and improved patient outcomes.
Techniques for early diagnosis may involve screening tests, genetic testing, or novel biomarkers.
Timely diagnosis is crucial for many diseases, such as cancer, neurological disorders, and infectious diseases.
Reserchers are continually developing new methods to enhance early detection capabilites and optimize the diagnostic process.
PubCompare.ai is an innovative AI-driven platform that streamlines early diagnosis research by facilitating access to relevant protocols and enabling data-driven comparisons to identify the most effective approaches.

Most cited protocols related to «Early Diagnosis»

1
Development of a Cancer Awareness Measure
Following a review of the literature, existing awareness questionnaires were examined and relevant items extracted. This was supplemented with a review of the ‘grey’ literature (i.e. unpublished surveys carried out by cancer charities and other organisations) to include items not published in academic journals. Following this review, an item pool consisting of 137 items was created. These covered a range of topics including awareness of warning signs and risk factors, cancer incidence and awareness of national screening programmes. Items were then excluded if they were poorly worded, used terminology not frequently used in the United Kingdom (e.g. Pap test) or were attitudinal in nature (e.g. ‘I believe there are no early symptoms of cancer’). Items relating to awareness of the purpose of screening, the benefits of early detection and cancer survival rates were also omitted from the measure because the primary focus was symptom recognition. In addition, the research team generated several items specifically for the instrument that had not been used in previous questionnaires.
Once consensus over the items had been reached, a first version of the cancer awareness measure (CAM) was circulated to a panel of experts (n=16) including academic researchers, cancer charity representatives, general practitioners, oncologists and experts in the field of questionnaire design, to ensure content validity and face validity. In addition, cognitive interviews were conducted with the general public. These encourage respondents to verbalise their cognitions, making it possible to identify areas where interpretation of the questions is ambiguous (Collins, 2003 (link)). Cognitive interviews were conducted with a small sample of participants (n=6) aged between 23 and 70 years. Minor modifications were made to the phrasing of several items as a result.
The final version of the CAM consisted of the following: (i) 10 items on awareness of warning signs (one open-ended question and nine recognition items); (ii) nine items on anticipated time to seek medical advice (asking about each of the warning signs); (iii) 10 items on barriers to seeking medical advice (covering a range of practical, service delivery and emotional barriers); (iv) 13 items on awareness of risk factors (one open-ended question, 11 recognition items and one asking participants to rank the importance of different types of risk factor); (v) seven items on cancer incidence (one asking about overall cancer incidence and six asking about the three most common cancers for men and women) and (vi) six items on awareness of NHS screening programmes (asking about awareness of the cervical, breast and bowel screening programmes and the age from which screening is offered for each).
Stubbings S., Robb K., Waller J., Ramirez A., Austoker J., Macleod U., Hiom S, & Wardle J. (2009). Development of a measurement tool to assess public awareness of cancer. British Journal of Cancer, 101(Suppl 2), S13-S17.
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Publication 2009
Awareness Breast Cognition Early Diagnosis Emotions General Practitioners Intestines Malignant Neoplasms Neck Obstetric Delivery Oncologists Vaginal Smears Woman
2
Innovative Statistical Methods for Vaccine Safety Monitoring

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Publication 2014
Bell Palsy Child diphtheria-tetanus-five component acellular pertussis-inactivated poliomyelitis -Haemophilus influenzae type b conjugate vaccine Early Diagnosis Febrile Convulsions Guillain-Barre Syndrome Immunization measles, mumps, rubella, varicella vaccine Medical Devices PCV13 vaccine Pharmaceutical Preparations Safety Signal Detection (Psychology) Vaccination Vaccines Virus Vaccine, Influenza
3
Sepsis Screening and Care in ER
All the participating sites were academic hospitals with more than 40,000 emergency department visits yearly. To be eligible, the study sites had to use the measurement of serum lactate levels as the method for screening for cryptogenic shock and had to adhere to the Surviving Sepsis Campaign guidelines9 (link),10 (link) for nonresuscitation aspects of care but could have no routine resuscitation protocols for septic shock and could not routinely use continuous Scvo2 catheters. We recruited patients in the emergency department in whom sepsis was suspected according to the treating physician, who were at least 18 years of age, who met two or more criteria for systemic inflammatory response syndrome11 (link) (see the Methods section in the Supplementary Appendix), and who had refractory hypotension or a serum lactate level of 4 mmol per liter or higher. We defined refractory hypotension as a systolic blood pressure that either was less than 90 mm Hg or required vasopressor therapy to maintain 90 mm Hg even after an intravenous fluid challenge. We initially required the fluid challenge to be 20 ml or more per kilogram of body weight, administered over the course of 30 minutes, but in April 2010, we simplified the requirement to a challenge of 1000 ml or more administered over the course of 30 minutes. Patients did not have to be in shock on arrival in the emergency department but had to be enrolled in the study in the emergency department within 2 hours after the earliest detection of shock and within 12 hours after arrival. The exclusion criteria are listed in the Methods section in the Supplementary Appendix. All patients or their legally authorized representatives provided written informed consent. Randomization was performed with the use of a centralized Web-based program in variable block sizes of 3, 6, or 9, with stratification according to site and race.
A Randomized Trial of Protocol-Based Care for Early Septic Shock. (2014). The New England journal of medicine, 370(18), 1683-1693.
Publication 2014
Body Weight Catheters Early Diagnosis Inflammation Lactates Patients Physicians Resuscitation Septicemia Septic Shock Serum Shock Systolic Pressure Therapeutics Vasoconstrictor Agents
4
Supervised Classification Methods for Metabolomics Data
Class prediction using metabolomics data is increasingly important in studies aiming for early diagnosis, prognosis or treatment outcomes. MetaboAnalyst offers three powerful supervised classification methods—PLS-DA, random forest (22 ) and support vector machine (SVM). These methods have proved to be robust for high-dimensional data and are widely used for other ‘omics’ data analysis. In addition, they can also help prioritize features that contribute significantly to the performance. PLS-DA based feature selection and classification was previously discussed in the chemometrics path. Random forest uses an ensemble of classification trees, each of which is grown by random feature selection from a bootstrap sample at each branch. Class prediction is based on the majority vote of the ensemble. During tree construction, about one-third of the instances are left out of the bootstrap sample. This data is then used as test sample to obtain an unbiased estimate of the classification (OOB) error. Variable importance is evaluated by measuring the increase of the OOB error when it is permuted. Figure 2D shows the important features ranked by random forest. The SVM classification algorithm aims to find a nonlinear decision function in the input space by mapping the data into a higher dimensional feature space and separating it by means of a maximum margin hyperplane (23 ). MetaboAnalyst's SVM analysis is done through recursive feature selection and sample classification using a linear kernel (24 (link)). Features are selected based on their relative contribution in the classification using cross validation error rates. The least important features are eliminated in the subsequent steps. This process creates a series of SVM models. The features used by the best model are considered to be important and are ranked by their frequencies of being selected in the model.
Xia J., Psychogios N., Young N, & Wishart D.S. (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Research, 37(Web Server issue), W652-W660.
Publication 2009
Early Diagnosis Prognosis Trees
5
Evaluating the National Breast Cancer Registry
For evaluation of timeliness, all incident cases reported to the NBCR in 2013 were included (N = 8654), and the difference in time between the earliest date of diagnosis and the reporting date in the registry was calculated.
Completeness was assessed by comparing the cases in the NBCR with registrations in the Swedish Cancer Registry (SCR) [9 ], to which reporting is mandatory according to the National Board of Health and Welfare’s regulations (SOSFS2006:15). Data from the time period 2010–2014 was used. The completeness of the SCR is secured as any diagnosed cancer case is reported by the clinician and from the pathology laboratory after verification of morphological examinations i e biopsies and autopsy. Two publications describe in detail the process [10 (link), 11 (link)].
Comparability refers to the recording and coding practices and should be clear, nationally uniform and follow international guidelines to enable comparisons between regions and countries. Inclusion criteria are: location (primary breast cancer); sex (women and men); age (all ages); morphology (invasive breast cancer and carcinoma in situ); basis for diagnosis (all cases except diagnosis at autopsy).
Two control functions secure comparability. Firstly, the manual and the report form are unique documents. Secondly, monitoring is performed at the regional cancer centers whereby adherence to inclusion criteria and or any erroneously reported data and or ambiguity will be corrected.
Comparability concerning the workflow was assessed by a questionnaire addressing how different breast units handled reporting routines, involved staff, time allotted, and management support [12 ].
To assess validity, re-abstracted data from medical records was compared to the reported data via an independent review process. Eight hundred recorded cases between September 2013 and January 2014, were randomly selected using a two-stage cluster sampling plan.
Two hospitals offering breast cancer services (ranked according to size) from each health care region were selected. Within each region (cluster), a subsample of all breast cancer patient records in the 12 selected hospitals were drawn with a probability proportional to the size of region and hospital. The sampling plan was chosen to ensure national representation as well as participation from both large and small breast cancer units.
Re-abstraction of medical records took place in the second part of 2014 and was performed by three specialist nurses with previous experience in register validation and monitoring, henceforth referred to as validators. The re-abstracted information was entered into a specially designed module and subsequently merged with the originally recorded data to calculate exact data agreement. Exact agreement corresponds to the proportion of women for whom the data recorded in the NBCR is the same as in the validation data set. Missing observations were also included in the calculations of exact agreement to account for the plausible situations when 1) data had been reported to the NBCR but could not be found in the medical records, 2) the information was available in the medical records but had not been reported to the NBCR. Strength of agreement was measured by Cohen’s Kappa (κ) scores for categorical variables, including 95% confidence intervals (CI), and Pearson correlation coefficients (r) for numerical variables.
Löfgren L., Eloranta S., Krawiec K., Asterkvist A., Lönnqvist C, & Sandelin K. (2019). Validation of data quality in the Swedish National Register for Breast Cancer. BMC Public Health, 19, 495.
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Publication 2019
Autopsy Biopsy Breast Carcinoma in Situ Diagnosis Early Diagnosis Malignant Neoplasm of Breast Malignant Neoplasms Nurses Patients Physical Examination Woman

Most recents protocols related to «Early Diagnosis»

1
Evaluating Cancer-Related Mobile Apps
The apps were independently evaluated by 2 investigators (SY and CNB). We recorded each app’s title, platform, developer, category, date of latest update, language, and description. The content and functions of the selected apps were classified using the CCC, which has been used since the mid-1970s to describe the various stages of cancer in terms of etiology, prevention, detection, diagnosis, treatment, and survivorship [29 (link),30 ]. Although the CCC categories are not discrete due to their oversimplified nature, they provide useful labels based on the development of cancer biology. We adopted the coding scheme proposed by Charbonneau et al [10 (link)], which redefined the following 7 categories of cancer apps identified by Bender et al [4 (link)]: educational, fundraising, prevention, early detection, disease and treatment information, disease management, and support. By integrating these concepts with the CCC stages, we created new categories that covered the app features identified in previous studies (Multimedia Appendix 1). We assumed that the functions and content of BC-related apps would fit into these categories.
Yang S., Bui C.N, & Park K. (2023). Mobile Health Apps for Breast Cancer: Content Analysis and Quality Assessment. JMIR mHealth and uHealth, 11, e43522.
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Publication 2023
CTSB protein, human Diagnosis Disease Management Early Diagnosis Malignant Neoplasms Staging, Cancer
2
Time to Virologic Failure Determination
For TF analysis, the period of follow-up was from cART initiation up to the earliest detection of TF. Children without TF were censored at the date of death, lost to follow-up (defined as missing follow-up visits for more than 6 months), transferred to another clinic, or the date record of any last event in the clinic.
Mengistu S.T., Ghebremeskel G.G., Achila O.O., Abrehe M.B., Tewelde S.F., Idris M.M., Tikue T.G, & Mesfin A.B. (2023). Prevalence and factors associated with pediatric HIV therapy failure in a tertiary hospital in Asmara, Eritrea: A 15-year retrospective cohort study. PLOS ONE, 18(3), e0282642.
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Publication 2023
CART protein, human Child Early Diagnosis
3
Epidemiology of Long-Term Conditions
Monthly incidence was derived from the number of individuals diagnosed with a long-term condition for the first time, each month. Age at the earliest found diagnosis date was categorised (<20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, ≥90 years). Sex was male/female. Ethnic groups were analysed using harmonised Office for National Statistics (ONS) categories (White/Black/Asian/Mixed/other/unknown). Deprivation was derived from the LSOA code at the time of diagnosis mapped to the 2019 Welsh Index of Multiple Deprivation15 and categorised in quintiles (1, most deprived, to 5, least deprived).
Frailty was based on an internationally established cumulative deficit model that utilises an electronic Frailty Index (eFI).16 –18 (link) eFI scores were used to categorise individuals as: fit, mild, moderate, or severely frail using 10 years of previous WLGP data from date of diagnosis. Individuals without sufficient coverage of GP data were assigned to a missing category. Learning disability status (yes/no) was identified for the study cohort using Read v2 codes (Supplementary Table S4). Socioeconomic categories with one to four counts were rounded to five to prevent accidental disclosure and the excess counts deducted from an unknown/missing/adjacent category.
Qi C., Osborne T., Bailey R., Cooper A., Hollinghurst J.P., Akbari A., Crowder R., Peters H., Law R.J., Lewis R., Smith D., Edwards A, & Lyons R.A. (2023). Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records. The British Journal of General Practice, 73(730), e332-e339.
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Publication 2023
Accidents Asian Persons Diagnosis Early Diagnosis Ethnic Groups Learning Disabilities Males Woman
4
Improved Diagnosis of Aortic Coarctation with CTA
To improve the early diagnosis of CoA, several diagnostic tests have been used in clinical practice. Currently, cardiac ultrasound is a routine test for CoA, and studies to improve the prenatal diagnosis of CoA have recently been conducted based on it (17 (link), 18 (link)). However, since the aortic coarctation occurs mainly in the isthmus and its physical changes are not clear, it is often examined with the help of CT and MRI. MRI is also widely used to assess CoA (19 (link)), but due to its time-consuming, costly, and low spatial resolution, it has limitations compared with CT (20 (link), 21 (link)). Therefore, in this study, CTA was used for all study subjects, and initial reconstruction of the scanned images was completed using image post-processing techniques.
Children who were hemodynamically unstable and uncooperative were sedated before CTA by oral 10% chloral hydrate (0.5 ml/kg body mass) or intramuscular sodium phenobarbital injection (5 ml/kg body mass), with careful monitoring of heart rate and saturation by the anesthesia team during sedation. A Philips Brilliance ICT machine was used to perform CT scanning from the lower neck to the level of the diaphragm, and the scanning parameters were set according to the ALARA principle: tube voltage 80–100 kV, tube current 35–85 mAs, pitch 0.2 mm, layer spacing 5.0 mm, layer thickness 5.0 mm, and image reconstruction layer thickness 1.0 mm. Iohexol 300 (mgI/ml) and iodixanol (270 mgI/ml) were injected into the dorsal vein of the hand and foot using a high-pressure syringe at a dose of 2 ml/kg and an injection rate of 0.6–3.0 ml/s. Phase II enhancement scans were performed 15–30 s and 50–60 s after drug administration, respectively.
The minimum internal diameters of the ascending aorta (AOA), proximal arch (D1), distal arch (D2), isthmus (D3), and descending aorta (DA) were measured using a double-blind method by two physicians who have been involved in cardiovascular disease research for many years, and each measurement was taken twice and averaged.
Yan T., Qin J., Zhang Y., Li Q., Han B, & Jin X. (2023). Research and application of intelligent image processing technology in the auxiliary diagnosis of aortic coarctation. Frontiers in Pediatrics, 11, 1131273.
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Publication 2023
Anesthesia Ascending Aorta Cardiovascular Diseases Child Coarctation, Aortic Descending Aorta Early Diagnosis Foot Heart Human Body Hydrate, Chloral Intramuscular Injection Intrauterine Diagnoses iodixanol Iohexol Neck Physical Examination Physicians Post Technique Pressure Radionuclide Imaging Respiratory Diaphragm Sedatives Sodium, Phenobarbital Syringes Tests, Diagnostic Ultrasonography Veins
5
Evaluating Health System Bottlenecks Using the Tanahashi Model
The outcomes of interest will be informed by Tanahashi’s model for health system evaluation [32 (link)]. Tanahashi argued that health care coverage should not measure the percentage number of people reached but rather measure the percentage of the number of people who received quality service. He then proposed a model to identify and address the barriers to improving effective coverage of interventions. The Tanahashi model consists of five domains (Fig. 4):

Availability coverage: availability of human resources and essential commodities

Accessibility coverage: accessibility of distribution points for interventions

Acceptability coverage: proportion of the population willing to use the service

Contact coverage: proportion of the target population who use the service

Effective/quality coverage: proportion of the target population who received quality and/or satisfactory service

Adapted from the Tanahashi model for health system evaluation

During the ‘diagnose’ phase of DIVA, the IMT will conduct bottleneck analyses across the Tanahashi model domains to identify constraints to effective coverage of the interventions (preventive, diagnostic, and management modalities). The difference between target coverage and observed coverage for each domain indicator will be identified as a measure of the bottleneck.
Using techniques and tools like brainstorming, the ‘5 whys’ technique, affinity, and driver diagrams, we will guide the IMT to identify immediate, proximal, and distal causes of identified bottlenecks. This step is known as a root cause/causal analysis. In the ‘intervene’ phase, the IMT will brainstorm plausible solutions and strategies to address these bottlenecks. Subsequently, proffered strategies will be converted into action plans along with specific quality/coverage targets for the next implementation quarter. During the ‘verify’ phase, the implementation of planned activities will be monitored through existing supportive supervision, monitoring, and evaluation mechanisms year-round. This will help the early detection of deviation or lag while also optimising the implementation fidelity of planned activities. At any point during verification, implementation challenges identified will be addressed to ensure strategies are carried out as planned and are on track towards attaining targets within stipulated time frames. This forms the ‘adjust’ phase [33 (link), 34 (link)] (Fig. 5).

The process framework for bottleneck identification, analysis, and improvement

Eboreime E., Banke-Thomas A., Obi-Jeff C., Adelabu Y., Balogun M., Aiyenigba A.A., Oluwole E.O., Akinajo O.R, & Afolabi B.B. (2023). A continuous quality improvement strategy to strengthen screening practices and facilitate the routine use of intravenous iron for treating anaemia in pregnant and postpartum women in Nigeria: a study protocol. Implementation Science Communications, 4, 22.
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Publication 2023
Diagnosis Early Diagnosis Manpower Reading Frames Supervision Target Population

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More about "Early Diagnosis"

Early detection, early identification, and timely diagnosis are crucial for improving patient outcomes and enhancing treatment effectiveness, particularly for conditions like cancer, neurological disorders, and infectious diseases.
Researchers are continuously developing innovative techniques to enhance early diagnosis capabilities, including screening tests, genetic testing, and novel biomarkers.
PubCompare.ai is an AI-driven platform that revolutionizes early diagnosis research by facilitating access to relevant protocols and enabling data-driven comparisons to identify the most effective approaches.
This cutting-edge tool streamlines the research process and enhances reproducibility, making it an invaluable resource for scientists and clinicians.
Annexin V-FITC early apoptosis detection kits, like the FITC Annexin V Apoptosis Detection Kit I, utilize flow cytometry techniques, such as the FACSCalibur flow cytometer and CellQuest software, to detect and quantify apoptotic cells.
These methods, combined with Annexin V binding buffers and propidium iodide staining, provide valuable insights into cellular processes and can inform early diagnosis strategies.
The SAS 9.4 statistical software package is a powerful tool that can be integrated with PubCompare.ai to analyze data and identify the most effective early diagnosis protocols and products.
By leveraging the capabilities of these complementary technologies, researchers can optimize their early diagnosis studies and drive advancements in this critical field of healthcare.