جستجوی مقالات مرتبط با کلیدواژه « decision support techniques » در نشریات گروه « پزشکی »

Just as failure to diagnose an acute ischemic stroke (AIS) in a timely manner affects the patient’s out-come; an inaccurate and misplaced impression of the AIS diagnosis is not without its drawbacks. Here, we introduce atwo-stage clinical tool to aid in the screening of AIS cases in need of imaging in the emergency department (ED).Meth-ods:This was a multicenter cross-sectional study, in which suspected AIS patients who underwent a brain magneticresonance imaging (MRI) were included. The 18 variables from nine existing AIS screening tools were extracted anda two-stage screening tool was developed based on expert opinion (stage-one or rule in stage) and multivariate logis-tic regression analysis (stage-two or rule out stage). Then, the screening performance characteristics of the two-stagemode was evaluated.

Data from 803 patients with suspected AIS were analyzed. Among them, 57.4% were male,and their overall mean age was 66.9 ± 13.9 years. There were 561 (69.9%) cases with a final confirmed diagnosis of AIS.The total sensitivity and specificity of the two-stage screening model were 99.11% (95% CI: 98.33 to 99.89) and 35.95%(95% CI: 29.90 to 42.0), respectively. Also, the positive and negative predictive values of two-stage screening model were78.20% (95% CI: 75.17 to 81.24) and 94.57% (95% CI: 89.93 to 81.24), respectively. The area under the receiver operatingcharacteristic (ROC) curve of the two-stage screening model for AIS was 67.53% (95% CI: 64.48 to 70.58). Overall, usingthe two-stage screening model presented in this study, more than 11% of suspected AIS patients were not referred forMRI, and the error of this model is about 5%.

Here, we proposed a 2-step model for approaching suspectedAIS patients in ED for an attempt to safely exclude patients with the least probability of having an AIS as a diagnosis.However, further surveys are required to assess its accuracy and it may even need some modifications.

We believe that designing a new tool which is comparable in terms of both sensitivity and specificity may play an important role in rapid and more accurate diagnosis of acute ischemic stroke (AIS) in prehospital stage. Therefore, we intended to develop a new clinical tool for the diagnosis of AIS in the prehospital stage.

This was a cross-sectional diagnostic accuracy study. All patients transferred to the emergency department (ED) who underwent brain magnetic resonance imaging (MRI) with impression of AIS were evaluated by 9 clinical tools for stroke diagnosis in the pre-hospital phase including Rapid Arterial Occlusion Evaluation (RACE), Cincinnati Prehospital Stroke Scale (CPSS), Los Angeles Prehospital Stroke Screen (LAPSS), Melbourne Ambulance Stroke Screen (MASS), Medic Prehospital Assessment for Code Stroke (Med PACS), Ontario Prehospital Stroke Screening Tool (OPSS), PreHospital Ambulance Stroke Test (PreHAST), Recognition of Stroke in the Emergency Room (ROSIER), and Face Arm Speech Test (FAST), and totally 19 items were reviewed and recorded. The new clinical tool was developed based on backward method of multivariable logistic regression analysis. The discrimination power of the new clinical tool for diagnosis of AIS was assessed with the area under the receiver operating characteristic curve (AUC-ROC).

Data from 806 patients were analyzed; of them, 57.4% were men. The mean age of the study patients was 66.9 years [standard deviation (SD) = 13.9]. In the multivariable model, 8 items remained. The AUC-ROC of the new clinical tool was 0.893 [95% confidence interval (CI): 0.869-0.917], and its best cut-off point was score ≥ 3 for positive AIS. At this cut-off point, sensitivity and specificity were 84.42% and 79.72%, respectively.

We introduced a new nomogram-based clinical tool for the diagnosis of AIS in the prehospital stage, which has acceptable specificity and sensitivity; moreover, it is comparable with previous tools.

Determining cardiovascular disease (CVD) research priorities is essential given the high burden of these diseases, limited financial resources, and competing priorities. This study aimed to determine the research priorities in CVD field in Iran using standard indigenous methods.

An extensive search was done in relevant international and national studies. Then, an indigenous standard multistage approach based on multicriteria decision analysis steps was adapted to local situation and implemented. This process included forming a working group of experts in priority setting methodology, identifying the context and prioritization framework, discussing the methodology with the National Network of CVD Research  NCVDR) members who ultimately determined the priority research topics, weighted topics criteria, ranked topics, and reviewed all determined  esearch priorities for final report.

Thirteen cardiovascular research priorities were determined by the NCVDR members. The first  ive priorities based on their scores include studies in hypertension, prevention and control of ischemic heart disease (IHD) and its risk  actors, burden of IHD, Registration of CVDs, and COVID?19 and CVDs.

Cardiovascular research priorities were determined using a standard indigenous approach by national experts who are the NCVDR members. These priorities can be used by researchers and health decision makers.

Head trauma is one of the most important causes of emergency department (ED) visits and the leading cause of disability and mortality in children. The aim of this study was to evaluate the Pediatric Emergency Care Applied Research Network (PECARN) rules to predict brain injuries in pediatrics with head trauma.

This descriptive–analytic study was performed on 250 pediatric patients with head trauma referred to Imam Reza Hospital in Tabriz City from August to September 2020. All patients were evaluated in the ED for the existing of any rules of the PECARN, then the results of the PECARN rules and brain computed tomography (CT) scan findings were compared in these patients. According to the PECARN rules, patients were classified into three categories, namely low, moderate, and high risk. For all three groups, if there is an indication of brain CT scan, it was performed and reported by an emergency medicine specialist.

The mean age of the patients was 88 months. In this study, 162 (64.8%), 42 (16.8%), and 46 (18.4%) patients were in the low‑risk, moderate‑risk, and high‑risk groups, respectively. Death was occurred in 18 (12.8%) patients. Results showed a statistically significant association between positive CT findings and some variables, such as behavioral change, vomiting, severe headache, LOC over 5 s, confusion, palpable skull fracture, skull base fracture, and the severe mechanism of injury (P < 0.05). Furthermore, a statistically significant association was found between PECARN rules and CT findings (P < 0.001).

According to the results of the present study, PECARN rules have a significant association with brain CT scan findings. Therefore, using these rules is recommended to reduce the number of brain CT scan requests for pediatric patients.

Resilience means the ability of a system to predict, tolerate, and adapt to various disturbances and recover quickly to its original state. This study aims to weigh and prioritize the indicators affecting the resilience in a combined cycle power plant using the combined method of FAHP-TOPSIS.

This is a descriptive-analytical and cross-sectional study conducted at the beginning of 2021 in the Kashan Combined Cycle Power Plant. In the first step, a literature review and semi-structured interviews with 25 experts were conducted to identify the indicators affecting resilience. A total of 20 affecting indicators were identified and divided into three groups: situational awareness, vulnerability, and adaptability. In the next step, we used the Fuzzy Analytical Hierarchy Process (FAHP) to determine the indicators’ weights of each group. In the end, we used the TOPSIS method to perform the final prioritization of the indicators.

The final results of prioritizing the indicators that affect resilience based on the outcomes of the TOPSIS method showed that the three indicators of structural stability (final weight=1), senior management awareness of the roles and responsibilities (final weight=0.075), and understanding and risk acceptance (final weight=0.067) play the most important roles, while logistics support index (final weight=0.029) is the least important indicator in determining the level of resilience.

By recognizing and prioritizing the indicators affecting the level of resilience, corrective and preventive measures can be defined and implemented to improve safety and increase the resilience in combined cycle power plants based on the importance of each indicator. Also, the method introduced in this paper can be used as a scientific technique to identify and prioritize resilience indicators in other process industries such as oil and gas and petrochemical industries.

Although various predictive instruments have been introduced for early stroke diagnosis, there is no consensus on their performance. Therefore, we decided to assess the value of predictive instruments in the detection of stroke by conducting an umbrella review. 

A search was performed in the Medline, Embase, Scopus and Web of Science databases by the end of August 2021 for systematic reviews and meta-analyses. Original articles included in the systematic reviews were retrieved, summarized and pooled sensitivity, specificity and diagnostic odds ratio were calculated. The level of evidence was divided into five groups: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV) and non-significant. 

The value of 33 predictive instruments was evaluated. The sample size included in these scoring systems’ assessments varied between 182 and 47072 patients. The level of evidence was class I in one tool, class II in 18 tools, class III in 2 tools, class IV in 11 tools, and non-significant in one tool. Apart from Med PACS, which had a low diagnostic value, other tools appeared to be able to detect a stroke. The optimum performance for diagnosis of stroke was for ROSIER, NIHSS, PASS, FAST, LAMS, RACE and CPSS. 

Convincing to suggestive evidence shows that ROSIER, NIHSS, PASS, FAST, LAMS, RACE and CPSS have the optimum performance in identifying stroke. Since ROSIER’s calculation is simple and has the highest sensitivity and specificity among those predictive instruments, it is recommended for stroke diagnosis in pre-hospital and in-hospital settings.

The participation of patients in treatment and nursing decision-making has been advocated by many medical staff. This is not only to attach importance to the wishes of patients, but also to the needs of social development. The purpose of this research was to investigate the attitudes of Chinese breast cancer medical staff towards the implementation of breast cancer decision-making aids.

A cross-sectional study was conducted among 420 doctors and nurses in the Department of Breast Surgery. We used a questionnaire designed by investigators. Data was collected from February 2021 to September 2021. IBM SPSS Version 22 was used to analyze the collected data.

Overall, 420 valid questionnaires were returned from 220 doctors and 200 nurses. Response rate was 85.19%. The results showed that 77.14% of the medical staff supported the promotion of breast cancer decision-making aids, and 85.71%（360/420）agreed that patients should be the main participants in high-quality clinical decision-making. Also, 95.24%（400/420）believed that patients should know the reasons for making treatment decisions, and agreed that the positive effects of patient decision-making aids were positively correlated with high education (r education=0.317, P=0.001). There were statistically significant differences in the attitudes of medical staff with different working years (X2=9.432, P=0.024), educational background (X2=42.918, P<0.001) and shared decision education (X2=11.932, P=0.008) on whether to promote decision-making aids.

At this stage, breast medical staff have a positive attitude towards using breast cancer decision aids for joint decision-making.

Patient decision aids are detailed and personalized health education materials that assist patients in decision making. According to expert viewpoints, this study aimed to determine important factors in implementing the prenatal screening decision aid in Iran.

Methods & Materials:

 In this qualitative study, 24 experts, including seven obstetricians, four information scientists, five managers or policymakers, and eight midwives, were selected using purposive and snowball sampling approaches. Semi-structured interviews were conducted to collect the data between January 2020 and June 2020 in Tehran, Iran. A prenatal screening decision aid was presented to the participants, and we asked them to raise their concerns and thoughts regarding the factors influencing the implementation of patient decision aids. We used MAXQDA 10 and applied conventional content analysis for data analysis.

Two organizational and personal factors themes were identified to implement Iran's prenatal screening decision aids.

We identified the viewpoints of experts regarding major factors in patient decision aids implementation for prenatal screening. Before implementing prenatal screening decision aids in Iran, it would be helpful to consider these organizational and personal factors. Prenatal screening decision aids can provide better information for pregnant women and strengthen their decision-making ability.

Stroke is one of the most common debilitating diseases. Although effective treatment is available, a golden time has been defined in this regard. Therefore, prompt action is needed to identify patients with stroke as soon as possible, even in the pre-hospital stage. In recent years, several clinical scales have been introduced for this purpose. We performed the present study to examine the accuracy of eight clinical scales in terms of stroke diagnosis.

This multicenter diagnostic accuracy study was conducted in 2019. All patients older than 18 years who were admitted to the emergency department (ED) and underwent brain magnetic resonance imaging (MRI) for a suspected stroke were eligible. All data were gathered through a pre-prepared checklist consisting of three sections, using the clinical records of the patients. The first section of the checklist included basic characteristics and demographic data. The second part included physical examination findings of 19 items related to the 8 scales. The third part was dedicated to the final diagnosis based on the interpretation of brain MRI, which was considered the gold standard for the diagnosis of acute ischemic stroke (AIS) in the current study.

The data from 805 patients suspected of stroke were analyzed. In all, 463 patients (57.5%) were male. The participants’ age was 6-95 years with a mean age of 66.9 years (SD = 13.9). Of all the registered patients, 562 (69.8%) had an AIS. The accuracy of screening tests was 63.0% to 84.4%. The sensitivity and specificity were 71.9% to 95.7% and 46.5% to 82.8%, respectively. Among all the screening tests, Los Angeles Pre-Hospital Stroke Screening (LAPSS) had the lowest sensitivity, and Medic Prehospital Assessment for Code Stroke (Med PACS) had the highest sensitivity. In addition, PreHospital Ambulance Stroke Test (PreHAST) had the lowest specificity and LAPSS had the highest specificity.

Based on the findings of the present study, highly sensitive tests that can be used in this regard are Cincinnati Prehospital Stroke Scale (CPSS), Face-Arm-Speech-Time (FAST), and Med PACS, all of which have about 95% sensitivity. On the other hand, none of the studied tools were desirable (specificity above 95%) in any of the examined cut-offs

Andsberg et al. have recently introduced a novel scoring system entitled “PreHospital Ambulance Stroke Test (PreHAST)”, which helps to early identification of patients with acute ischemic stroke (AIS) even in prehospital setting. Its validity has not been assessed in a study yet, and the purpose of this study was to assess this scoring system on a larger scale to provide further evidence in this regard.

This was a cross-sectional multi-center accuracy study, in which, sampling was performed prospectively. All patients over 18 years of age admitted to the emergency department (ED) and suspected as AIS cases were included. All required data were recorded in a form consisting of 3 parts: baseline characteristics, neurological examination findings required for calculating PreHAST score, and the ultimate diagnosis made from interpretation of their brain magnetic resonance imaging (MRI).

Data from 805 patients (57.5% men) with the mean age of 67.1 ± 13.6 years were analyzed. Of all the patients presenting with suspected AIS, 562 (69.8%) had AIS based on their MRI findings. At the suggested cut-off point (score ≥ 1), PreHAST had a specificity of 46.5% [95% confidence interval (CI): 40.1%-53.0%) and a sensitivity of 93.2% (95% CI: 90.8%-95.2%).

According to the findings of our study, at the suggested cut-off point (score ≥ 1), PreHAST had 93.2% sensitivity and 46.5% specificity in detection of patients with AIS, which were somewhat different from those reported in the original study, where 100% sensitivity and 40% specificity were reported for this scoring system.

 Limitations of Glasgow coma scale (GCS) led the researchers to designing new physiologic scoring systems such as revised trauma score (RTS), rapid acute physiology score (RAPS) and rapid emergency medicine score (REMS), and worthing physiological scoring system (WPSS). However, it is not yet known whether these models have any advantage over GCS. 

The present study attempted to compare the values of 4 physiologic scoring systems including RTS, RAPS, REMS and WPSS with GCS in predicting in-hospital mortality of trauma patients. 

The present diagnostic accuracy study was performed on trauma patients presenting to emergency departments of 4 hospitals in Iran throughout 2017. Patients were clinically evaluated and were followed until discharge from hospital. Finally, the status of patients regarding mortality and poor outcome (death, vegetative status, severe disability, and moderate disability) was recorded and predictive value of GCS was compared with physiologic scales. 

Area under the ROC curve of GCS in prediction of in-hospital mortality was not significantly different from that of REMS (0.89 vs. 0.91; p=0.298), RAPS (0.89 vs. 0.88; p=0.657), and WPSS (0.89 vs 0.91; p=0.168) but was significantly more than RTS (0.89 vs. 0.85; p=0.002). In addition, area under the ROC curves of GCS, REMS, RAPS, WPSS and RTS in prediction of poor outcome were 0.89, 0.88, 0.88, 0.91, and 0.81, respectively. Area under the ROC curve of GCS in prediction of poor outcome did not differ from area under the ROC curves of REMS (0.89 vs. 0.88; p=0.887), RAPS (0.89 vs. 0.88; p=0.601) and WPSS (0.89 vs. 0.91; p=0.113) but was significantly higher than RTS (0.89 vs. 0.81; p<0.0001). 

Findings of the present study indicated that GCS is still the best method for evaluating injury severity and trauma patients’ outcome in the emergency department; because it is easier to calculate and assess than many physiologic scales and it has a better performance in predicting in-hospital mortality and poor outcome compared to RTS.

Predicting the outcome of patientswith intracranial hemorrhage (ICH) is the area of interest for in charge physicians aswell as patients and their associates. This study aimed to evaluate the accuracy of SUSPEKT score in predicting one-month outcome of patients with hemorrhagic stroke.

This prospective cross sectional study was conducted on > 18 years old patients with non-traumatic supra-tentorial ICH admitted to emergency department, from February 2017 to January 2018. SUSPEKT score was measured for each patient and its screening performance characteristics in prediction of one-month mortality were calculated.

169 cases with the mean age of 63.09 § 15.45 (21 – 96) years were studied (56.8% male). After one month follow up 47 (27.8%) cases had died, 30 (17.7%) cases were bed ridden, and 72 (42.6%) could walk without help or with a cane. Non-survived patients had significantly larger intra-ventricular hemorrhage (IVH) (p < 0.001) and hematoma (p < 0.001) volume, higher serumglucose (p < 0.001) and blood pressure (p = 0.028), higher frequency of IVH (p < 0.001), and higher WBC count (p = 0.037). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SUSPEKT score at the 65 cut point were 82.97% (95% CI: 68.65% – 91.86%), 74.59% (95% CI: 65.76% – 81.84%), 55.71% (95% CI: 43.38% – 67.40%), and 91.91% (95% CI: 84.23% – 96.16%), respectively.

Total accuracy of SUSPEKT score in predicting one-month mortality of non- traumatic ICH patients is in good range and it has 82%sensitivity and 92%NPV in this regard. It seems thatwe need further studies before applying the score in routine practice.

There is still no consensus on the value of Manchester Acute Coronary Syndromes (MACS) deci-sion rule in detecting acute coronary syndrome (ACS). Therefore, the purpose of the present systematic reviewand meta-analyzes is to summarize the clinical evidence in the evaluation of the value of MACS in the diagno-sis of ACS.

A literature search was performed on the Medline, Embase, Scopus, and Web of Sciencedatabases. Outcomes included acute myocardial infarction (AMI) and major adverse cardiac event (MACE).Data were analyzed in the STATA 14.0 statistical program and the results were reported as summary receiver op-erating characteristics (SROC), sensitivity, specificity, positive and negative likelihood ratio, and diagnostic oddsratio with 95% confidence interval (95% CI).

Finally, 8 articles included in the meta-analysis. The areaunder the SROC of MACS was excellent in rule out of AMI (AUC = 0.99, 95% CI: 0.97 to 0.99) and MACE (AUC= 0.97, 95% CI: 0.95 to 0.98). The sensitivity and specificity of the troponin-only MACS / history electrocardio-gram alone MACS (HE-MACS) in the rule out of AMI were 0.99 (95% CI: 0.98-0.99) and 0.22 (95% CI: 0.11-0.37),respectively, and for the original MACS were in order 0.99 (95% CI: 0.98-0.99) and 0.26 (95% CI: 0.20-0.34). Thesensitivity and specificity of the troponin-only MACS / HE-MACS in the rule out of MACE were 0.94 (95% CI:0.92-0.96) and 0.22 (95% CI: 0.12-0.39) compared to the 0.99 (95% CI: 0.98-0.99) and 0.27 (95% CI: 0.22-0.33) forthe original MACS.

The findings of this study showed that original MACS, troponin-only MACS, andHE-MACS are able to rule out AMI and MACE. However, further studies are needed in developing countries toconfirm its external validity.