Advancements in Varicose Vein Treatment: Anatomy, Pathophysiology, Minimally Invasive Techniques, Sclerotherapy, Patient Satisfaction, and Future Directions.

Varicose veins are a common vascular condition known for causing discomfort and cosmetic concerns. This comprehensive narrative review delves into their anatomy, pathophysiology, and modern treatment options, with a focus on endovenous techniques and sclerotherapy. The review starts by emphasizing the intricate anatomy of lower extremity venous circulation, underlining the significance of both superficial and deep venous networks in venous return. It also addresses how changes in the venous wall, including valvular insufficiency, contribute to the development of varicose veins. Endovenous techniques like endovenous laser ablation (EVLA), radiofrequency ablation (RFA), and mechanochemical endovenous ablation (MOCA) are explored in detail. These minimally invasive procedures have revolutionized varicose vein treatment, offering high success rates and quicker recovery compared to traditional surgery. The review also highlights their efficacy and safety profiles, aiding clinicians in informed decision-making. Sclerotherapy, a vital modality for varicose veins, is thoroughly examined, covering both liquid and foam sclerotherapy. Foam sclerotherapy, in particular, is recognized for its improved outcomes. The review provides a comprehensive comparison of these treatment modalities, highlighting differences in technical success, recurrence rates, and cost-effectiveness. Patient preferences and satisfaction play a significant role in choosing the right treatment. Safety and potential complications associated with these treatments are explored, with a focus on minor issues and rare adverse events. This review also emphasizes the positive impact of varicose vein interventions on patients' quality of life.

Unraveling the Gut-Brain Axis in Multiple Sclerosis: Exploring Dysbiosis, Oxidative Stress, and Therapeutic Insights.

This comprehensive review delves into the intricate relationship between the gut microbiota and multiple sclerosis (MS), shedding light on the potential therapeutic avenues for this complex autoimmune disease. It emphasizes the multifactorial nature of MS, including genetic, environmental, and gender-related factors. Furthermore, the article highlights the emerging role of gut microbiota in MS pathophysiology, particularly in terms of gut dysbiosis, oxidative stress, and inflammasome activation within the gut-brain axis. This interplay raises intriguing questions about how the gut microbiota influences the onset and progression of MS. Environmental factors, such as diet and pollutants, add further layers of complexity to the connection between gut health and MS risk. This review also discusses promising therapeutic interventions, such as fecal microbiota transplantation, probiotics, dietary adjustments, and gut-derived metabolites that offer potential avenues for managing MS. It underscores the need for ongoing research to fully unravel the complexities of the role of the gut-brain axis in MS. Ultimately, this article provides a comprehensive exploration of the topic, offering hope for novel preventive and therapeutic strategies that could significantly improve the lives of individuals affected by this challenging autoimmune condition.

Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments.

OBJECTIVE: Diagnostic errors are major contributors to preventable patient harm. We validated the use of an electronic health record (EHR)-based trigger (e-trigger) to measure missed opportunities in stroke diagnosis in emergency departments (EDs). METHODS: Using two frameworks, the Safer Dx Trigger Tools Framework and the Symptom-disease Pair Analysis of Diagnostic Error Framework, we applied a symptom-disease pair-based e-trigger to identify patients hospitalized for stroke who, in the preceding 30 days, were discharged from the ED with benign headache or dizziness diagnoses. The algorithm was applied to Veteran Affairs National Corporate Data Warehouse on patients seen between 1/1/2016 and 12/31/2017. Trained reviewers evaluated medical records for presence/absence of missed opportunities in stroke diagnosis and stroke-related red-flags, risk factors, neurological examination, and clinical interventions. Reviewers also estimated quality of clinical documentation at the index ED visit. RESULTS: We applied the e-trigger to 7,752,326 unique patients and identified 46,931 stroke-related admissions, of which 398 records were flagged as trigger-positive and reviewed. Of these, 124 had missed opportunities (positive predictive value for "missed" = 31.2%), 93 (23.4%) had no missed opportunity (non-missed), 162 (40.7%) were miscoded, and 19 (4.7%) were inconclusive. Reviewer agreement was high (87.3%, Cohen's kappa = 0.81). Compared to the non-missed group, the missed group had more stroke risk factors (mean 3.2 vs 2.6), red flags (mean 0.5 vs 0.2), and a higher rate of inadequate documentation (66.9% vs 28.0%). CONCLUSION: In a large national EHR repository, a symptom-disease pair-based e-trigger identified missed diagnoses of stroke with a modest positive predictive value, underscoring the need for chart review validation procedures to identify diagnostic errors in large data sets.

Use of patient complaints to identify diagnosis-related safety concerns: a mixed-method evaluation.

BACKGROUND: Patient complaints are associated with adverse events and malpractice claims but underused in patient safety improvement. OBJECTIVE: To systematically evaluate the use of patient complaint data to identify safety concerns related to diagnosis as an initial step to using this information to facilitate learning and improvement. METHODS: We reviewed patient complaints submitted to Geisinger, a large healthcare organisation in the USA, from August to December 2017 (cohort 1) and January to June 2018 (cohort 2). We selected complaints more likely to be associated with diagnostic concerns in Geisinger's existing complaint taxonomy. Investigators reviewed all complaint summaries and identified cases as 'concerning' for diagnostic error using the National Academy of Medicine's definition of diagnostic error. For all 'concerning' cases, a clinician-reviewer evaluated the associated investigation report and the patient's medical record to identify any missed opportunities in making a correct or timely diagnosis. In cohort 2, we selected a 10% sample of 'concerning' cases to test this smaller pragmatic sample as a proof of concept for future organisational monitoring. RESULTS: In cohort 1, we reviewed 1865 complaint summaries and identified 177 (9.5%) concerning reports. Review and analysis identified 39 diagnostic errors. Most were categorised as 'Clinical Care issues' (27, 69.2%), defined as concerns/questions related to the care that is provided by clinicians in any setting. In cohort 2, we reviewed 2423 patient complaint summaries and identified 310 (12.8%) concerning reports. The 10% sample (n=31 cases) contained five diagnostic errors. Qualitative analysis of cohort 1 cases identified concerns about return visits for persistent and/or worsening symptoms, interpersonal issues and diagnostic testing. CONCLUSIONS: Analysis of patient complaint data and corresponding medical record review identifies patterns of failures in the diagnostic process reported by patients and families. Health systems could systematically analyse available data on patient complaints to monitor diagnostic safety concerns and identify opportunities for learning and improvement.

Identifying psychiatric diagnostic errors with the Safer Dx Instrument.

OBJECTIVE: Diagnostic errors in psychiatry are understudied partly because they are difficult to measure. The current study aimed to adapt and test the Safer Dx Instrument, a structured tool to review electronic health records (EHR) for errors in medical diagnoses, to evaluate errors in anxiety diagnoses to improve measurement of psychiatric diagnostic errors. DESIGN: The iterative adaptation process included a review of the revised Safer Dx-Mental Health Instrument by mental health providers to ensure content and face validity and review by a psychometrician to ensure methodologic validity and pilot testing of the revised instrument. SETTINGS: None. PARTICIPANTS: Pilot testing was conducted on 128 records of patients diagnosed with anxiety in integrated primary care mental health clinics. Cases with anxiety diagnoses documented in progress notes but not included as a diagnosis for the encounter (n = 25) were excluded. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): None. RESULTS: Of 103 records meeting the inclusion criteria, 62 likely involved a diagnostic error (42 from use of unspecified anxiety diagnosis when a specific anxiety diagnosis was warranted; 20 from use of unspecified anxiety diagnosis when anxiety symptoms were either undocumented or documented but not severe enough to warrant diagnosis). Reviewer agreement on presence/absence of errors was 88% (κ = 0.71). CONCLUSION: The revised Safer Dx-Mental Health Instrument has a high reliability for detecting anxiety-related diagnostic errors and deserves testing in additional psychiatric populations and clinical settings.

Oncogenic enhancer of zeste homolog 2 is an actionable target in patients with non-small cell lung cancer.

BACKGROUND: The aims of this study were to investigate the link between enhancer of zeste homolog 2 (EZH2) and histone deacetylase (HDAC) in preclinical studies and in human lung cancer tissue microarrays. METHODS: Enhancer of zeste homolog 2 and HDAC1 mRNA expression in two lung adenocarcinoma (LUAD) datasets (MDACC and TCGA) were correlated with patient outcomes. We evaluated the association of EZH2 and HDAC1 expression with response to the HDAC1 inhibitor, suberoylanilide hydroxamic acid (SAHA). The response to SAHA was assessed at baseline and after alteration of EZH2 or HDAC mRNA expression in LUAD cell lines. RESULTS: Direct correlation was found between EZH2 and HDAC1 expression (P < 0.0001). When EZH2 expression was knocked down- or upregulated, there was a corresponding decrease or increase in expression of HDAC expression, respectively. Cell lines with high EZH2 expression responded to SAHA treatment with a mean inhibition rate of 73.1% compared to 43.2% in cell lines with low EZH2 expression (P < 0.0001). This correlation was confirmed in non-small cell lung cancer (NSCLC) specimens from MDACC (Spearman's correlation r = 0.416; P < 0.0001) and TCGA datasets (r = 0.221; P < 0.0001). Patients with high EZH2 and high HDAC1 expression in stage I NSCLC specimens of both datasets had the lowest survival compared to the patients with low expression of either or both markers. CONCLUSION: Our findings show that overexpression of EZH2 is a negative prognostic indicator. Increased EZH2 expression predicts for response to HDAC inhibitors and thus could serve as a biomarker for selecting NSCLC patients for treatment with HDAC inhibitors.

Electronic health record reviews to measure diagnostic uncertainty in primary care.

RATIONALE, AIMS AND OBJECTIVES: Diagnostic uncertainty is common in primary care. Because it is challenging to measure, there is inadequate scientific understanding of diagnostic decision-making during uncertainty. Our objective was to understand how diagnostic uncertainty was documented in the electronic health record (EHR) and explore a strategy to retrospectively identify it using clinician documentation. METHODS: We reviewed the literature to identify documentation language that could identify both direct expression and indirect inference of diagnostic uncertainty and designed an instrument to facilitate record review. Direct expression included clinician's use of question marks, differential diagnoses, symptoms as diagnosis, or vocabulary such as "probably, maybe, likely, unclear or unknown," while describing the diagnosis. Indirect inference included absence of documented diagnosis at the end of the visit, ordering of multiple consultations or diagnostic tests to resolve diagnostic uncertainty, and use of suspended judgement, test of treatment, and risk-averse disposition. Two physician-reviewers independently reviewed notes on a sample of outpatient visits to identify diagnostic uncertainty at the end of the visit. Documented Ninth Revision of the International Classification of Diseases (ICD-9) diagnosis codes and note quality were assessed. RESULTS: Of 389 patient records reviewed, 218 had evidence of diagnostic activity and were included. In 156 visits (71.6%), reviewers identified clinicians who experienced diagnostic uncertainty with moderate inter-reviewer agreement (81.7%; Cohen's kappa: 0.609). Most cases (125, 80.1%) showed evidence of both direct expression and indirect inference. Uncertainty was directly expressed in 139 (89.1%) cases, most commonly by using symptoms as diagnosis (98, 62.8%), and inferred in 144 (92.3%). In more than 1/3 of visits (58, 37.2%), diagnostic uncertainty was recorded inappropriately using ICD-9 codes. CONCLUSIONS: While current diagnosis coding mechanisms (ICD-9 and ICD-10) are unable to capture uncertainty, our study finds that review of EHR documentation can help identify diagnostic uncertainty with moderate reliability. Better measurement and understanding of diagnostic uncertainty could help inform strategies to improve the safety and efficiency of diagnosis.

An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients.

BACKGROUND: Methods to identify preventable adverse events typically have low yield and efficiency. We refined the methods of Institute of Healthcare Improvement's Global Trigger Tool (GTT) application and leveraged electronic health record (EHR) data to improve detection of preventable adverse events, including diagnostic errors. METHODS: We queried the EHR data repository of a large health system to identify an 'index hospitalization' associated with care escalation (defined as transfer to the intensive care unit (ICU) or initiation of rapid response team (RRT) within 15 days of admission) between March 2010 and August 2015. To enrich the record review sample with unexpected events, we used EHR clinical data to modify the GTT algorithm and limited eligible patients to those at lower risk for care escalation based on younger age and presence of minimal comorbid conditions. We modified the GTT review methodology; two physicians independently reviewed eligible 'e-trigger' positive records to identify preventable diagnostic and care management events. RESULTS: Of 88 428 hospitalisations, 887 were associated with care escalation (712 ICU transfers and 175 RRTs), of which 92 were flagged as trigger-positive and reviewed. Preventable adverse events were detected in 41 cases, yielding a trigger positive predictive value of 44.6% (reviewer agreement 79.35%; Cohen's kappa 0.573). We identified 7 (7.6%) diagnostic errors and 34 (37.0%) care management-related events: 24 (26.1%) adverse drug events, 4 (4.3%) patient falls, 4 (4.3%) procedure-related complications and 2 (2.2%) hospital-associated infections. In most events (73.1%), there was potential for temporary harm. CONCLUSION: We developed an approach using an EHR data-based trigger and modified review process to efficiently identify hospitalised patients with preventable adverse events, including diagnostic errors. Such e-triggers can help overcome limitations of currently available methods to detect preventable harm in hospitalised patients.

Development and Validation of Trigger Algorithms to Identify Delays in Diagnostic Evaluation of Gastroenterological Cancer.

BACKGROUND & AIMS: Colorectal cancer (CRC) and hepatocellular cancer (HCC) are common causes of death and morbidity, and patients benefit from early detection. However, delays in follow-up of suspicious findings are common, and methods to efficiently detect such delays are needed. We developed, refined, and tested trigger algorithms that identify patients with delayed follow-up evaluation of findings suspicious of CRC or HCC. METHODS: We developed and validated two trigger algorithms that detect delays in diagnostic evaluation of CRC and HCC using laboratory, diagnosis, procedure, and referral codes from the Department of Veteran Affairs National Corporate Data Warehouse. The algorithm initially identified patients with positive test results for iron deficiency anemia or fecal immunochemical test (for CRC) and elevated α-fetoprotein results (for HCC). Our algorithm then excluded patients for whom follow-up evaluation was unnecessary, such as patients with a terminal illness or those who had already completed a follow-up evaluation within 60 days. Clinicians reviewed samples of both delayed and nondelayed records, and review data were used to calculate trigger performance. RESULTS: We applied the algorithm for CRC to 245,158 patients seen from January 1, 2013, through December 31, 2013 and identified 1073 patients with delayed follow up. In a review of 400 randomly selected records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 56.0% (95% CI, 51.0%-61.0%). We applied the algorithm for HCC to 333,828 patients seen from January 1, 2011 through December 31, 2014, and identified 130 patients with delayed follow-up. During manual review of all 130 records, we found that our algorithm identified patients with delayed follow-up with a positive predictive value of 82.3% (95% CI, 74.4%-88.2%). When we extrapolated the findings to all patients with abnormal results, the algorithm identified patients with delayed follow-up evaluation for CRC with 68.6% sensitivity (95% CI, 65.4%-71.6%) and 81.1% specificity (95% CI, 79.5%-82.6%); it identified patients with delayed follow-up evaluation for HCC with 89.1% sensitivity (95% CI, 81.8%-93.8%) and 96.5% specificity (95% CI, 94.8%-97.7%). Compared to nonselective methods, use of the algorithm reduced the number of records required for review to identify a delay by more than 99%. CONCLUSIONS: Using data from the Veterans Affairs electronic health record database, we developed an algorithm that greatly reduces the number of record reviews necessary to identify delays in follow-up evaluations for patients with suspected CRC or HCC. This approach offers a more efficient method to identify delayed diagnostic evaluation of gastroenterological cancers.

Electronic Triggers to Identify Delays in Follow-Up of Mammography: Harnessing the Power of Big Data in Health Care.

PURPOSE: We previously developed electronic triggers to automatically flag records for patients experiencing potential delays in diagnostic evaluation for certain cancers. Because of the unique clinical, logistic, and legal aspects of mammography, this study was conducted to evaluate the effectiveness of a trigger to flag delayed follow-up on mammography. METHODS: An algorithm was developed to detect delays in follow-up of abnormal mammographic results (>60 days for BI-RADS® 0, 4, and 5 and >7 months for BI-RADS 3) using clinical data in the electronic health record. Flagged records were then manually reviewed to determine the trigger's performance characteristics (positive and negative predictive value, sensitivity, and specificity). The frequency of delays and patient communication related to abnormal results, reasons for lack of follow-up, and whether patients were subsequently diagnosed with breast cancer were also assessed. RESULTS: Of 365,686 patients seen between January 1, 2010, and May 31, 2015, the trigger identified 2,129 patients with abnormal findings on mammography, of whom it flagged 552 as having delays in follow-up. From these, review of 400 randomly selected records revealed 283 true delays (positive predictive value, 71%; 95% confidence interval, 66%-75%), including 280 records without any documented plan and three patients with plans that were not adhered to. Transcription and reporting inconsistencies were identified in 27% of externally performed mammographic reports. Only 335 records (84%) contained specific documentation that the patient was informed of the abnormal result. CONCLUSIONS: Care delays appear to continue despite federal laws requiring patient notification of mammographic results within 30 days. Clinical application of mammography-related triggers could help detect these delays.

Application of Electronic Algorithms to Improve Diagnostic Evaluation for Bladder Cancer.

BACKGROUND: Strategies to ensure timely diagnostic evaluation of hematuria are needed to reduce delays in bladder cancer diagnosis. OBJECTIVE: To evaluate the performance of electronic trigger algorithms to detect delays in hematuria follow-up. METHODS: We developed a computerized trigger to detect delayed follow-up action on a urinalysis result with high-grade hematuria (>50 red blood cells/high powered field). The trigger scanned clinical data within a Department of Veterans Affairs (VA) national data repository to identify all patient records with hematuria, then excluded those where follow-up was unnecessary (e.g., terminal illness) or where typical follow-up action was detected (e.g., cystoscopy). We manually reviewed a randomly-selected sample of flagged records to confirm delays. We performed a similar analysis of records with hematuria that were marked as not delayed (non-triggered). We used review findings to calculate trigger performance. RESULTS: Of 310,331 patients seen between 1/1/2012-12/31/2014, the trigger identified 5,857 patients who experienced high-grade hematuria, of which 495 experienced a delay. On manual review of 400 randomly-selected triggered records and 100 non-triggered records, the trigger achieved positive and negative predictive values of 58% and 97%, respectively. CONCLUSIONS: Triggers offer a promising method to detect delays in care of patients with high-grade hematuria and warrant further evaluation in clinical practice as a means to reduce delays in bladder cancer diagnosis.