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Acute Disease
Acute Disease
Acute Disease: A rapid-onset, severe, and typically short-term medical condition that requires prompt attention.
This may include infectious diseases, traumatic injuries, and other health problems that develop suddenly and have the potential for serious complications if not properly managed.
Effective research and treatment approaches are crucial to address the unique challenges posed by acute diseases and improve patient outcomes.
This may include infectious diseases, traumatic injuries, and other health problems that develop suddenly and have the potential for serious complications if not properly managed.
Effective research and treatment approaches are crucial to address the unique challenges posed by acute diseases and improve patient outcomes.
Most cited protocols related to «Acute Disease»
Acute Disease
Adult
Antibodies
Autonomic Nervous System Disorders
Child
Cognition
Cyclophosphamide
Encephalitis
Ethics Committees, Research
Hypoventilation
Immunotherapy
Intravenous Immunoglobulins
Memory
Movement Disorders
N-Methyl-D-Aspartate Receptors
Neoplasms
Patient Representatives
Patients
Physicians
Plasmapheresis
Puberty
Relapse
Rituximab
Seizures
Speech
Steroids
Youth
We aimed to provide evidence that the ICD-10-CM phecode map resulted in phenotypes like those sourced from the ICD-9-CM phecode map. First, we selected 357,728 patients in the VUMC EHR who had ≥1 ICD-9-CM and ≥1 ICD-10-CM codes in two 18-month windows. We selected windows to occur prior to and after VUMC’s transition to ICD-10-CM. To reduce potential confounders, we left a 6-month buffer after ICD-9-CM was replaced with ICD-10-CM. Further, the ICD-10-CM observation window ended before VUMC switched from its locally developed EHR [27 (link)] to the Epic system. This created two windows ranging from January 1, 2014 to June 30, 2015 for ICD-9-CM, and January 1, 2016 to June 30, 2017 for ICD-10-CM (Figure 3 ). The final cohort consisted of 55.10% (197,109/357,728) females with mean age of 45 (SD 25) years old. From the two observation periods, we extracted all ICD-9-CM and ICD-10-CM codes for each patient. We then mapped these codes to phecodes using the ICD-9-CM phecode [14 ] and ICD-10-CM phecode maps.
We used the patient cohort to test our hypothesis that the ICD-10-CM phecode map created phenotype definitions that were comparable to those generated using the gold-standard ICD-9-CM phecode map. For this analysis, we used four common chronic diseases (Hypertension, Hyperlipidemia, Type 1 Diabetes, and Type 2 Diabetes) and chose one acute disease (Intestinal infection) as a negative control. We expected that a large majority of the chronic disease patients and a small minority of the acute disease patients from the ICD-9-CM era would reproduce the same phenotypes during the ICD-10-CM era. We defined the phenotype cases as follows: Hypertension with phecodes 401.* (* means one or more digits or a period); Hyperlipidemia, phecodes 272.*; Type 1 diabetes, phecodes 250.1*; Type 2 diabetes, phecodes 250.2*; Intestinal infection, phecodes 008.*.
For each phenotype, we reported the number of ICD-9-CM cases and the number of those individuals who were also ICD-10-CM cases. To identify the possible reasons for individuals who were not identified as phenotype cases in the ICD-10-CM period, two authors with clinical training (PW, WQW) manually reviewed the EHRs of ten randomly selected patients from each chronic disease group, except Type 1 diabetes, for a total of thirty patients.
We used the patient cohort to test our hypothesis that the ICD-10-CM phecode map created phenotype definitions that were comparable to those generated using the gold-standard ICD-9-CM phecode map. For this analysis, we used four common chronic diseases (Hypertension, Hyperlipidemia, Type 1 Diabetes, and Type 2 Diabetes) and chose one acute disease (Intestinal infection) as a negative control. We expected that a large majority of the chronic disease patients and a small minority of the acute disease patients from the ICD-9-CM era would reproduce the same phenotypes during the ICD-10-CM era. We defined the phenotype cases as follows: Hypertension with phecodes 401.* (* means one or more digits or a period); Hyperlipidemia, phecodes 272.*; Type 1 diabetes, phecodes 250.1*; Type 2 diabetes, phecodes 250.2*; Intestinal infection, phecodes 008.*.
For each phenotype, we reported the number of ICD-9-CM cases and the number of those individuals who were also ICD-10-CM cases. To identify the possible reasons for individuals who were not identified as phenotype cases in the ICD-10-CM period, two authors with clinical training (PW, WQW) manually reviewed the EHRs of ten randomly selected patients from each chronic disease group, except Type 1 diabetes, for a total of thirty patients.
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Acute Disease
Buffers
Diabetes Mellitus, Insulin-Dependent
Diabetes Mellitus, Non-Insulin-Dependent
Disease, Chronic
Females
Fingers
Gold
High Blood Pressures
Hyperlipidemia
Infection
Intestines
Microtubule-Associated Proteins
Minority Groups
Patients
Phenotype
Patient data were recorded by using either online data collection software (provided by iMDsoft, Tel Aviv, Israel) or the SAPS 3 stand-alone database system (provided by the CCC). The latter software used a Microsoft Access database (Microsoft Corporation, Redmond, WA, USA) for data storage and needed no Internet connection for data entry. Both systems maintained a variety of plausibility controls to ensure the quality of the recorded data. Each variable was precisely defined before the start of data collection (see Appendix C of the ESM). Detailed definitions of the variables were available to participants in both paper and electronic form. To facilitate plausibility checking, each variable was assigned a probability range, encompassing the range of probable values for that variable. In addition, a range of possible values (storage range) for that variable was defined (e.g., for FiO2, no values <21% or >100% could be accepted). Thus, formal plausibility controls in the software systems were used wherever possible and ensured the maximum of data quality checking during data collection.
Participants who could not use one of the two software options were allowed to record the data on paper forms and submit them to the CCC (n=26 ICUs). Patient data were then entered into the SAPS 3 stand-alone software system and thus checked for plausibility. In cases of uncertainty, ICU coordinators were contacted for clarification.
In addition, each ICU received a questionnaire with detailed questions about ICU structures and about resources available in other areas of the hospital.
Data were collected at ICU admission, on days 1, 2 and 3, and on the last day of the ICU stay. Data from the day of admission (aside from sociodemographic data such as age and sex) were categorized into different levels: (i) data about the condition of the patient before ICU admission, such as chronic conditions and medical diseases; (ii) data about the patient’s condition at ICU admission, such as the reason for admission, infection at admission, and surgical status; and (iii) data about the patient’s physiologic derangement at ICU admission. These data were collected within an hour before or after ICU admission.
On the following days of the ICU stay, further information was collected: severity of illness, as measured by the SAPS II [5 (link)]; number and severity of organ dysfunction, as measured by the Sequential Organ Failure Assessment (SOFA) [28 ]; length of ICU and hospital stay; and outcome data, including vital status at ICU and hospital discharge. All patients were subjected to mandatory follow-up until hospital discharge, but not longer than 90 days after ICU admission. Patients still remaining in the hospital at 90 days were at that time classified as being “still in the hospital”.
To record diagnoses, a three-level system was used. (i) An acute medical disease was recorded for all patients, independent of surgical status, i.e., the acute (or acute on chronic) disease that best explained the ICU admission. If the reason for ICU admission was infectious disease, then this was recorded. (ii) Surgical status at admission and the anatomic site of surgery were recorded for all patients undergoing surgery during the hospital stay before ICU admission. (iii) A concrete reason for admission had to be selected. At least one reason for admission was required, but several selections were possible (one within each organ system). If no other reason was present, at least “basic and observational care” had to be selected.
All participants received detailed documentation of patient- and ICU-based data items as well as a detailed description of the data collection process. Moreover, specific forms to check the completeness of the patient-based documentation were provided. Additionally, a training session for ICU coordinators was organised at the 15th Annual Congress of the ESICM in Barcelona, Spain, before the start of data collection. Throughout the project, the project website (http://www.saps3.org ) provided all necessary information. In addition, the CCC was available to answer questions by email, fax and phone. Data were to be collected from all consecutively admitted patients between 14 October and 15 December 2002. ICUs with a high number of beds (and thus also admissions) could stop patient enrolment after contributing 100 patients.
Participants who could not use one of the two software options were allowed to record the data on paper forms and submit them to the CCC (n=26 ICUs). Patient data were then entered into the SAPS 3 stand-alone software system and thus checked for plausibility. In cases of uncertainty, ICU coordinators were contacted for clarification.
In addition, each ICU received a questionnaire with detailed questions about ICU structures and about resources available in other areas of the hospital.
Data were collected at ICU admission, on days 1, 2 and 3, and on the last day of the ICU stay. Data from the day of admission (aside from sociodemographic data such as age and sex) were categorized into different levels: (i) data about the condition of the patient before ICU admission, such as chronic conditions and medical diseases; (ii) data about the patient’s condition at ICU admission, such as the reason for admission, infection at admission, and surgical status; and (iii) data about the patient’s physiologic derangement at ICU admission. These data were collected within an hour before or after ICU admission.
On the following days of the ICU stay, further information was collected: severity of illness, as measured by the SAPS II [5 (link)]; number and severity of organ dysfunction, as measured by the Sequential Organ Failure Assessment (SOFA) [28 ]; length of ICU and hospital stay; and outcome data, including vital status at ICU and hospital discharge. All patients were subjected to mandatory follow-up until hospital discharge, but not longer than 90 days after ICU admission. Patients still remaining in the hospital at 90 days were at that time classified as being “still in the hospital”.
To record diagnoses, a three-level system was used. (i) An acute medical disease was recorded for all patients, independent of surgical status, i.e., the acute (or acute on chronic) disease that best explained the ICU admission. If the reason for ICU admission was infectious disease, then this was recorded. (ii) Surgical status at admission and the anatomic site of surgery were recorded for all patients undergoing surgery during the hospital stay before ICU admission. (iii) A concrete reason for admission had to be selected. At least one reason for admission was required, but several selections were possible (one within each organ system). If no other reason was present, at least “basic and observational care” had to be selected.
All participants received detailed documentation of patient- and ICU-based data items as well as a detailed description of the data collection process. Moreover, specific forms to check the completeness of the patient-based documentation were provided. Additionally, a training session for ICU coordinators was organised at the 15th Annual Congress of the ESICM in Barcelona, Spain, before the start of data collection. Throughout the project, the project website (
Acute Disease
Body Regions
Chronic Condition
Communicable Diseases
Diagnosis
Infection
Operative Surgical Procedures
Patient Discharge
Patients
physiology
Acute Disease
Caucasoid Races
Chest Pain
Closure, Hospital
Coronary Arteriosclerosis
Diagnosis
Electrocardiogram
Electrocardiography
Heart
Hospitalization
Long-Term Care
Nitrates
Patient Admission
Patient Discharge
Patients
Data were derived from the 2008 National Health Services Survey (NHSS), which has been organised by the Chinese Ministry of Health (MoH) every fifth year since 1993. The surveys were carried out from mid-June till mid-July, and face-to-face interviews were conducted by trained local interviewers [45 ]. The NHSS 2008 questionnaire includes more than 200 questions, on acute diseases and injuries, chronic and other diseases, hospitalisation, health-related behaviour, educational level, family income and employment status, social relations, safety and security, medical care fees, accessibility (distance and time) and satisfaction with health service, insurance coverage, vaccination and disease control, woman and child health services. In 2008, the EQ-5D was included for the first time.
In NHSS 2008, 56,400 households were sampled using a multi-stage stratified cluster random sampling [46 ]. In the first sample stage, 2,400 counties were stratified based on socio-economic, health care and population structure to sample 94 counties. In the second stage, 2,350 streets (urban area) and townships (rural area) in the 94 counties were stratified based on population size and income per capita to sample 470 streets and townships. In the third stage, 940 residential committees (urban area) and villages (rural area) were sampled using the same criteria as in the second stage. In each residential committee or village, 60 households were randomly selected, and all family members in a sampled household were interviewed individually. EQ-5D was asked among persons aged 15 years and over, and no upper-age limit was applied. However, the instrument can be used in younger age groups, and the newly developed child-friendly version of the EQ-5D, named EQ-5D-Y [47 (link), 48 ], is available in some language versions, but not yet in Chinese. Hence, persons aged under 15 years were not included in this study.
In total, 177,501 respondents were included in NHSS 2008. Of these, about 18% aged below 15 years were excluded, since EQ-5D questions should only be administered to respondents aged 15 years and over. Respondents not answering the questions by themselves were excluded (13%). In total, less than 2% of the respondents had missing answers on age, sex, in at least one of the EQ-5D dimensions, on VAS or reported VAS higher than 100. After applying the previous exclusion criteria, 120,703 respondents were included in this study.
Ethical permission was granted by the Regional ethics committee, Stockholm, Sweden, for analyses of this study (Dnr: 2009/1892–31).
In NHSS 2008, 56,400 households were sampled using a multi-stage stratified cluster random sampling [46 ]. In the first sample stage, 2,400 counties were stratified based on socio-economic, health care and population structure to sample 94 counties. In the second stage, 2,350 streets (urban area) and townships (rural area) in the 94 counties were stratified based on population size and income per capita to sample 470 streets and townships. In the third stage, 940 residential committees (urban area) and villages (rural area) were sampled using the same criteria as in the second stage. In each residential committee or village, 60 households were randomly selected, and all family members in a sampled household were interviewed individually. EQ-5D was asked among persons aged 15 years and over, and no upper-age limit was applied. However, the instrument can be used in younger age groups, and the newly developed child-friendly version of the EQ-5D, named EQ-5D-Y [47 (link), 48 ], is available in some language versions, but not yet in Chinese. Hence, persons aged under 15 years were not included in this study.
In total, 177,501 respondents were included in NHSS 2008. Of these, about 18% aged below 15 years were excluded, since EQ-5D questions should only be administered to respondents aged 15 years and over. Respondents not answering the questions by themselves were excluded (13%). In total, less than 2% of the respondents had missing answers on age, sex, in at least one of the EQ-5D dimensions, on VAS or reported VAS higher than 100. After applying the previous exclusion criteria, 120,703 respondents were included in this study.
Ethical permission was granted by the Regional ethics committee, Stockholm, Sweden, for analyses of this study (Dnr: 2009/1892–31).
Acute Disease
Age Groups
Child
Child Health Services
Chinese
Face
Head
Health Services, National
Households
Injuries
Interviewers
Regional Ethics Committees
Safety
Satisfaction
Secure resin cement
Vaccination
Woman
Youth
Most recents protocols related to «Acute Disease»
Seven databases (EMbase, PubMed, Cochrane Library, Web of Science, CNKI, VIP, and Wan fang Database) were searched by computer,
The retrieval period was from database construction until August 2022; (III)Pubmed search strategy: (((((acute disease[MeSH Terms])) AND (cerebral infarction[MeSH Terms])) OR (acute cerebral infarction)) OR ((((acute disease[MeSH Terms])) AND (brain infarction[MeSH Terms])) OR (acute brain infarction))) AND (((((3-n-butylphthalide[MeSH Terms])) OR (3-n-butylphthalide[Title/Abstract])) OR (butylphthalide)) OR (NBP)).
The retrieval period was from database construction until August 2022; (III)Pubmed search strategy: (((((acute disease[MeSH Terms])) AND (cerebral infarction[MeSH Terms])) OR (acute cerebral infarction)) OR ((((acute disease[MeSH Terms])) AND (brain infarction[MeSH Terms])) OR (acute brain infarction))) AND (((((3-n-butylphthalide[MeSH Terms])) OR (3-n-butylphthalide[Title/Abstract])) OR (butylphthalide)) OR (NBP)).
3-n-butylphthalide
Acute Cerebrovascular Accidents
Acute Disease
Brain Infarction
cDNA Library
Cerebral Infarction
This clinical-based cross-sectional study included outpatients with T2DM being under treatment at the referral diabetes clinic of Sayad Shirazi hospital in Gorgan in 2021. The sample size was calculated based on this formula: N = (pq/e2) * z1−/α2. Here, p was the anticipation of anemia prevalence of 19.6% in the diabetic population, [14 ] q = 1 - p; e was an allowable error (5%); and Z1−α/2= 1.96. So, 242 participants were an acceptable sample size for this study.
To mitigate selection bias, patients were selected by a systematic random sampling technique among volunteer patients with T2DM. In this method, the selection of the first subject is done randomly and then the subsequent subjects are selected by a periodic process.
The exclusion criteria were: (1) age ≤ 18 or ≥ 75 years, (2) T2DM duration less than 1 year (3) known hematologic diseases (thalassemia, lymphoma, and leukemia) or other systemic disorders (such as infectious diseases) that could result in anemia, (4) presence of an acute condition (such as acute bleeding) or hospitalization within the last two weeks before sampling, (5) blood transfusions in the three months before sampling, (6) pregnancy, (7) type 1 DM, (8) smoking, (9) missing clinical and demographic data. Finally, 415 (109 men) eligible participants remained for our analysis.
The Ethics Committee of the Golestan University of Medical Sciences approved this study (ethics Code: IR.GOUMS.REC.1398.170). Verbal informed consent was obtained from all subjects, and they were also assured that their personal information would remain confidential. All methods of this study were performed in accordance with the relevant guidelines and regulations.
To mitigate selection bias, patients were selected by a systematic random sampling technique among volunteer patients with T2DM. In this method, the selection of the first subject is done randomly and then the subsequent subjects are selected by a periodic process.
The exclusion criteria were: (1) age ≤ 18 or ≥ 75 years, (2) T2DM duration less than 1 year (3) known hematologic diseases (thalassemia, lymphoma, and leukemia) or other systemic disorders (such as infectious diseases) that could result in anemia, (4) presence of an acute condition (such as acute bleeding) or hospitalization within the last two weeks before sampling, (5) blood transfusions in the three months before sampling, (6) pregnancy, (7) type 1 DM, (8) smoking, (9) missing clinical and demographic data. Finally, 415 (109 men) eligible participants remained for our analysis.
The Ethics Committee of the Golestan University of Medical Sciences approved this study (ethics Code: IR.GOUMS.REC.1398.170). Verbal informed consent was obtained from all subjects, and they were also assured that their personal information would remain confidential. All methods of this study were performed in accordance with the relevant guidelines and regulations.
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Acute Disease
Anemia
Blood Transfusion
Communicable Diseases
Diabetes Mellitus
Ethics Committees
Hematological Disease
Hospital Administration
Hospitalization
Hospital Referral
Leukemia
Lymphoma
Outpatients
Patients
Pregnancy
Thalassemia
Voluntary Workers
The information about the intervention would be sent to various high school institutions across Lithuania. The invitations to participate in the study and intervention would be spread in collaboration with contact persons in schools.
The sample will comprise self-referred adolescents who experience high levels of stress in daily life. The inclusion criteria for the participation: (1) adolescents aged 15–19, (2) studying at the high school in Lithuania; (3) comprehending Lithuanian, (4) parental and own consent for minor-aged participants (< 18 years) or only own consent for adolescents aged 18 years or older to participate in the study provided, (5) have access to a device (such as a tablet, phone or computer) with an Internet connection. Exclusion criteria: (1) acute psychiatric condition/crisis, (2) no recent life-stressor exposure, or low levels of stress.
The sample will comprise self-referred adolescents who experience high levels of stress in daily life. The inclusion criteria for the participation: (1) adolescents aged 15–19, (2) studying at the high school in Lithuania; (3) comprehending Lithuanian, (4) parental and own consent for minor-aged participants (< 18 years) or only own consent for adolescents aged 18 years or older to participate in the study provided, (5) have access to a device (such as a tablet, phone or computer) with an Internet connection. Exclusion criteria: (1) acute psychiatric condition/crisis, (2) no recent life-stressor exposure, or low levels of stress.
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Acute Disease
Adolescent
Medical Devices
Parent
Tablet
All patients were admitted to our neurology department between 1 July 2020 and 1 July 2021, with a diagnosis of a first-time stroke. From July 2021 to July 2022, young and middle-aged patients with stroke who had been discharged from the electronic medical system for 1 year were eligible to be surveyed by telephone. Of the 1,136 patients recorded in the electronic medical record system, 789 patients were included in this study based on the following criteria: (i) first stroke, (ii) the diagnosis of stroke (hemorrhage stroke, ischemic stroke, or hemorrhagic stroke combined with ischemic stroke), (iii) working age (18–59 years for men and 18–54 years for women) at the stroke onset, and (iv) active employment status (full-time or part-time competitive employment, or self-employment) at the stroke onset. We excluded patients who had stopped working before the onset and those with other critical illnesses, such as heart failure, respiratory failure, malignant tumors, severe trauma, and other acute diseases.
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Acute Disease
Cerebrovascular Accident
Congestive Heart Failure
Critical Illness
Diagnosis
Hemorrhagic Stroke
Malignant Neoplasms
Patients
Respiratory Failure
Stroke, Ischemic
Woman
Wounds and Injuries
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Acute Disease
Adult
Birth
Congenital Disorders
Ethanol
Fetus
Hemoglobin
High-Risk Pregnancy
Infant
Infant, Newborn
Inflammation
Mothers
Pharmaceutical Preparations
Pregnancy
Urinary Tract Infection
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More about "Acute Disease"
Acute diseases are a class of medical conditions characterized by a rapid onset, severe symptoms, and a typically short duration.
These conditions often require prompt attention and effective treatment to prevent serious complications.
Synonyms for acute diseases include sudden illness, acute health crisis, and emergency medical situations.
Related terms encompass infectious diseases, traumatic injuries, and other health problems that develop abruptly and have the potential for severe consequences if not properly managed.
Abbreviations commonly associated with acute diseases include AD, ER, and ICU.
Key subtopics within the realm of acute diseases include infectious agents (e.g., Bordetella pertussis toxin), treatment approaches (e.g., penicillin, streptomycin, L-glutamine, RPMI-1640 culture medium), and supportive care (e.g., DMSO, Female Hartley guinea pigs, SAS 9.4).
Effective research and treatment strategies are crucial to address the unique challenges posed by acute diseases and improve patient outcomes.
Tools like PubCompare.ai can enhance the reproducibility and accuracy of acute disease research by helping scientists effortlessly locate relevant protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products for their research needs.
These conditions often require prompt attention and effective treatment to prevent serious complications.
Synonyms for acute diseases include sudden illness, acute health crisis, and emergency medical situations.
Related terms encompass infectious diseases, traumatic injuries, and other health problems that develop abruptly and have the potential for severe consequences if not properly managed.
Abbreviations commonly associated with acute diseases include AD, ER, and ICU.
Key subtopics within the realm of acute diseases include infectious agents (e.g., Bordetella pertussis toxin), treatment approaches (e.g., penicillin, streptomycin, L-glutamine, RPMI-1640 culture medium), and supportive care (e.g., DMSO, Female Hartley guinea pigs, SAS 9.4).
Effective research and treatment strategies are crucial to address the unique challenges posed by acute diseases and improve patient outcomes.
Tools like PubCompare.ai can enhance the reproducibility and accuracy of acute disease research by helping scientists effortlessly locate relevant protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products for their research needs.