After the completion of a clinical trial, its conclusion generally depends on the results of statistical analysis of the main outcome, that is, whether the P-value in the hypothesis test is less than the α level of the hypothesis test, usually α=0.05. The size of the P-value indicates the sufficient degree of reason for making the hypothesis judgment, and can be interpreted as to determine whether a conclusion is statistically significant but does not involve the difference in the degree of drug effects or other effects. Fragility index, which is, the minimum number of patients required to change the occurrence of a target outcome event to a non-target outcome event from a statistically significant outcome to a non-significant outcome, can be used to assist in understanding of clinical trial statistical inference results and assisting in clinical decision making This paper discusses the concept, calculation method and clinical application of the fragility index, and recommends that the fragility index be routinely reported in all future randomized controlled trials to help patient clinicians and policymakers make appropriate and optimal decisions.
Objective To apply the method of evidence-based medicine to identify the best therapy option for an emergency patient with upper gastrointestinal hemorrhage. Methods According to time and logical sequence of clinical events, a complete decision tree was built after the following steps to find the best treatment: clear decision-making, drawing decision tree graphics, listing the outcome probability, giving appropriate values to the final outcome, calculating and determining the best strategies. Results The performance of endoscopic therapy for the patient with upper gastrointestinal hemorrhage within the first six hours had little effect on the prognosis. Interventional therapy after the failure of endoscopic therapy had less mortality than direct surgical exploration. Conclusion Making clinical decision analyses via drawing the decision tree can help doctors clarify their ideas, get comprehensive views of clinical problems, and ultimately choose the best treatment strategy for patients.
Objective To investigate the decision-making situation of doctors in the township hospitals in Gaolan, Gansu province, and to discuss its scientificity and rationality. Methods Self-designed questionnaire was adopted to investigate the clinical decision-making situation of 108 doctors from 7 township hospitals in Gaolan county. The investigation contained three parts as follows: basic information of respondents, general information of clinical decision-making evidence, and comparison between respondents’ decision-making situation and current best clinical evidence. Results Among the total 108 questionnaires distributed, 89 valid were retrieved. The feedback showed that 79% of the doctors diagnosed and treated patients in accordance with medical textbooks; 53% took curative effect into consideration in the first place; 33% failed to consider patients’ willingness properly when making clinical decisions; and 52% made clinical therapy regimen for common diseases based on the evidence which was different from that in BMJ published Clinical Evidence. Conclusion While making clinical decisions, doctors in the township hospitals do not adequately refer to the best clinical evidence as their decision-making basis, and fail to take patients’ value and willingness into consideration properly. It is necessary to promote the concept of evidence-based medicine and spread the best evidence in the township health departments.
Generative artificial intelligence (AI) technology plays a significant role in enhancing data application capabilities, improving disease diagnosis and treatment plans, and advancing health management, drug development, genetic analysis, and precision medicine. However, due to the diagnostic complexity, treatment diversity, and high technical demands of orthopedic diseases, the application of generative AI in orthopedics is still in its early exploration stage. This paper, based on the experience of applying generative AI, summarizes the concept, working principles, progress of application in orthopedics, as well as the existing shortcomings and optimization strategies, aiming to provide valuable insights for the application of generative AI in orthopedics clinical practice.
ObjectiveTo investigate the influence of misplaced subclavian vein (SCV) catheter into the ipsilateral internal jugular vein (IJV) on transpulmonary thermodilution (TPTD) measurements and explore the possible mechanisms preliminarily.MethodsIn this prospective study, 408 patients in whom an SCV catheterization was indicated for TPTD monitoring were enrolled. A first set of TPTD measurements was collected at baseline in all patients (group 1, SCV catheters were correctly placed; group 2, SCV catheters were misplaced into the ipsilateral IJV). The parameters included mean transit time (MTt), downslope time (DSt), cardiac index (CI), global end-diastolic volume index (GEDVI) and extra-vascular lung water index (EVLWI). A second set of TPTD measurements was performed only in those with catheter misplacement immediately after the misplaced SCV catheters being corrected (Group 3). The differences in MTt, DSt, GEDVI and EVLWI between group 2 and 3 were recorded as ΔMTt, ΔDSt, ΔGEDVI and ΔEVLWI, respectively.ResultsGEDVI and EVLWI were significantly higher (all P<0.001) in group 2 than those in group 1, while CI was not significantly different (P>0.05) between these two groups. Multivariate logistic regression identified PaO2/FiO2 [adjusted odds ratio (OR) 1.492/10 mm Hg; 95% confidence interval (CI), 1.180 - 1.884; P<0.001], GEDVI (OR=1.307/10 mL/m2, 95% CI 1.131 - 1.511; P<0.001) and EVLWI (OR=3.05; 95%CI 1.593 - 5.840; P<0.001) as the 3 independent factors associated with the misplacement of SCV catheter into the ipsilateral IJV. In group 2, GEDVI [(1041±122)mL/m2 vs. (790±102)mL/m2, P<0.001], EVLWI [(20.3±4.0)mL/kg vs. (10.3±2.3)mL/kg, P<0.001], CI [(3.6±1.2)L·min–1·m–2 vs. (2.9±1.0)L·min–1·m–2, P<0.001], MTt [(38.2±13.3)s vs. (30.8±9.4)s, P<0.001] and DSt [(18.9±7.2)s vs. (13.2±4.9)s, P<0.001)] were significantly higher than those in Group 3. Multiple regression analysis demonstrated that ΔEVLWI (R2=0.86, P<0.001) was negatively correlated with ΔMTt (coefficient±SE, –0.52±0.12; P<0.001) and positively correlated with ΔDSt (coefficient±SE, 1.45±0.17; P<0.001).ConclusionsDuring TPTD measurements, indicator injection through an SCV catheter misplaced into the ipsilateral IJV results in an overestimation of CI, GEDVI and EVLWI. The increase in DSt might be a key factor in explaining the overestimation of EVLWI in patients with misplaced SCV catheters. Given that the accurate measurements of GEDVI and EVLWI are of utmost importance for guiding resuscitation and decision-making regarding fluids administration, immediate repositioning is required if a misplacement is suspected and confirmed by the chest X-ray.
Objective To summarize the classic and latest treatment techniques for localized knee cartilage lesions in clinical practice and create a new comprehensive clinical decision-making process. Methods The advantages and limitations of various treatment methods for localized knee cartilage lesions were summarized by extensive review of relevant literature at home and abroad in recent years. Results Currently, there are various surgical methods for treating localized knee cartilage injuries in clinical practice, each with its own pros and cons. For patients with cartilage injuries less than 2 cm2 and 2-4 cm2 with bone loss are recommended to undergo osteochondral autograft (OAT) and osteochondral allograft (OCA) surgeries. For patients with cartilage injuries less than 2 cm2 and 2-4 cm2 without bone loss had treatment options including bone marrow-based techniques (micro-fracture and ogous matrix induced chondrogenesis), autologous chondrocyte implantation (ACI)/matrix-induced ACI, particulated juvenile allograft cartilage (PJAC), OAT, and OCA. For patients with cartilage injuries larger than 4 cm2 with bone loss were recommended to undergo OCA. For patients with cartilage injuries larger than 4 cm2 without bone loss, treatment options included ACI/matrix-induced ACI, OAT, and PJAC. Conclusion There are many treatment techniques available for localized knee cartilage lesions. Treatment strategy selection should be based on the size and location of the lesion, the extent of involvement of the subchondral bone, and the level of evidence supporting each technique in the literature.
ObjectivesTo provide an overview of whether the clinical decision support system (CDSS) was effective in reducing medication error and improving medication safety and to assess the quality of available scientific evidence.MethodsPubMed, EMbase, The Cochrane Library, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect systematic reviews (SRs) on application of clinical decision support system in the medication error and safety from January, 1996 to November, 2018. Two reviewers independently screened literature, extracted data and then evaluated methodological quality of included SRs by using AMSTAR tool.g AMSTAR tool.ResultsA total of 20 SRs including 256 980 healthcare practitioners and 1 683 675 patients were included. Specifically, 16 studies demonstrated moderate quality and 4 demonstrated high quality. 19 SRs evaluated multiple process of care outcome: 9 were sufficient evidence, 6 were limited evidence, and 7 were insufficient evidence which proved that CDSS had a positive effect on process outcome. 13 SRs evaluated reported patient outcomes: 1 with sufficient evidence, 3 with limited evidence, and 9 without sufficient evidence.ConclusionsCDSS reduces medication error by inconsistently improving process of care measures and seldom improving patient outcomes. Larger samples and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on patient outcomes.
Guideline implementation with decision support checklist (GUIDES) aims to assist the self-reflection of evidence-based clinical decision support system (CDSS) related professionals to enhance the process monitor and continuous improvement of evidence-based CDSS. This paper interpreted the development process, target user, and assessment method of GUIDES, analyzed the practical value of GUIDES through a typical example, and then reflected on the GUIDES and current studies on evidence-based CDSS in China. It is expected to provide references for future studies.
Artificial intelligence (AI) is reshaping evidence-based clinical decision-making. From the perspective of clinical decision-making, this paper explores the collaborative value of AI in life-cycle health management. While AI can enhance early disease screening efficiency (e.g., medical image analysis) and assist clinical decision-making through personalized health recommendations, its reliance on non-specialized data necessitates the development of dedicated AI systems grounded in high-quality, specialty-specific evidence. AI should serve as an auxiliary tool to evidence-based clinical decision-making, with physicians’ comprehensive judgment and humanistic care remaining central to medical decision-making. Clinicians must improve the reliability of decision making through refining prompt design and cross-validating AI outputs, while actively participate in AI tool optimization and ethical standard development. Future efforts should focus on creating specialty-specific AI tools based on high-quality evidence, establishing dynamic guideline update systems, and formulating medical ethical standards to position AI as a collaborative partner for physicians in implementing life-cycle health management.