Volume 9, Issue 4

Hypernatremia occurs when the plasma sodium concentration is greater than 145 mmol/L. Depending on the duration, hypernatremia can be differentiated into acute and chronic. According to severity: mild, moderate and threatening hypernatremia. Finally, depending on pathogenesis, hypernatremia can be defined as hypervolemic, hypovolemic, and euvolemic. Acute hypervolemic hypernatremia is often secondary to increased sodium intake (hypertonic NaCl and NaHCO 3 solutions). Instead, chronic hypervolemic hypernatremia may be an expression of primary hyperaldosteronism. Euvolemic hypernatremia occurs in diabetes insipidus: depending on the underlying pathogenesis, it can be classified into two basic types: neurogenic (or central) and nephrogenic. The neurogenic form may be triggered by traumatic, vascular or infectious events; the nephrogenic form may be due to pharmacological causes, such as lithium, or non-pharmacological ones, such as hypokalemia. For hypovolemic hypernatremia, possible explanations are renal or extrarenal losses. The main goal of treatment of hypernatremia is the restoration of plasma tonicity. In particular, if the imbalance has occurred acutely, rapid correction improves the prognosis by preventing the effects of cellular dehydration; if hypernatremia has developed slowly, over a period of days, a slow correction rate (no more than 0.4 mmol/L/h) is recommended.

High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The ‘omics’ technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.

Premature closure is often described as a significant contributor to diagnostic error. Therefore, developing strategies to mitigate premature closure could reduce diagnostic errors and improve patient care. Here we propose the novel concept of pursuit of an “endpoint diagnosis” as a cognitive forcing strategy (CFS) for avoiding premature diagnostic closure. We define an “endpoint diagnosis” as an underlying causative explanation for a patient’s signs, symptoms, and laboratory and radiographic data that exhausts additional relevant diagnostic evaluation. We have observed four contexts in which the error of not pursuing an endpoint diagnosis most often occurs: (1) diagnoses that appear to result in the same treatment regardless of etiology, (2) cases that are particularly complex, (3) clinical scenarios that are vulnerable to systems errors, and (4) situations in which patients’ problems are attributed to uncontrolled underlying risk factors or an exacerbation of a known condition. Additionally, we address why we believe endpoint diagnoses are not universally pursued, delineate when this approach might be particularly useful, attempt to reconcile the potential conflict between accepting diagnostic ambiguity in certain instances and pursuing endpoint diagnoses, and outline possible concerns that might arise with using this CFS, including the possibility of lengthy evaluations resulting in overdiagnosis and overtreatment. Our overarching goal is for this CFS to help clinicians in their daily clinical practice as they seek to optimize their diagnostic skill and patient care.

Solving diagnostic errors is difficult and progress on preventing those errors has been slow since the 2015 National Academy of Medicine report. There are several methods used to improve diagnostic and other errors including voluntary reporting; malpractice claims; patient complaints; physician surveys, random quality reviews and audits, and peer review data which usually evaluates single cases and not the systems that allowed the error. Additionally, manual review of charts is often labor intensive and reviewer dependent. In 2010 we developed an e-Autopsy/e-Biopsy (eA/eB) methodology to aggregate cases with quality/safety/diagnostic issues, focusing on a specific population of patients and conditions. By performing a hybrid review process (cases are first filtered using administrative data followed by standardized manual chart reviews) we can efficiently identify patterns of medical and diagnostic error leading to opportunities for system improvements that have improved care for future patients. We present a detailed methodology for eA/eB studies and describe results from three successful studies on different diagnoses (ectopic pregnancy, abdominal aortic aneurysms, and advanced colon cancer) that illustrate our eA/eB process and how it reveals insights into creating systems that reduce diagnostic and other errors. The eA/eB process is innovative and transferable to other healthcare organizations and settings to identify trends in diagnostic error and other quality issues resulting in improved systems of care.

Objectives Management reasoning has not been widely explored but likely requires broader abilities than diagnostic reasoning. An enhanced understanding of management reasoning could improve medical education and patient care. We conducted a novel exploratory study to gain further insights into procedure-based management reasoning. Methods Participant physicians managed a simulated patient who acutely decompensates in a team-based, time-pressured, live scenario. Immediately following the scenario, physicians perform a think-aloud protocol by watching video recordings of their performance and narrating their reflections in real-time. Verbatim transcripts of the think-aloud protocol were inductively coded using a constant comparative method and evaluated for themes. Results We recruited 19 physicians (15 internal medicine, one family medicine, and three general surgery) for this study. Recognizing that diagnostic and management reasoning intertwine, this paper focuses on management reasoning’s characteristics. We developed three categories of management reasoning factors with eight subthemes. These are Patient factors: Acuity and Preferences; Physician factors: Recognized Errors, Anxiety, Metacognition, Monitoring, and Threshold to Treat; and one Environment factor: Resources. Conclusions Our findings on procedure-based management reasoning are consistent with Situation Awareness and Situated Cognition models and the extant work on management reasoning, demonstrating that management is inherently complex and contextually bound. Unique to this study, all physicians focused on prognosis, indicating that attaining competency in procedural management may require planning and prediction abilities. Physicians also expressed concerns about making mistakes, potentially resulting from the scenario’s emphasis on a procedure and our physicians’ having less expertise in the treatment of tension pneumothorax.

Objectives To test a structured electronic health record (EHR) case review process to identify diagnostic errors (DE) and diagnostic process failures (DPFs) in acute care. Methods We adapted validated tools (Safer Dx, Diagnostic Error Evaluation Research [DEER] Taxonomy) to assess the diagnostic process during the hospital encounter and categorized 13 postulated e-triggers. We created two test cohorts of all preventable cases (n=28) and an equal number of randomly sampled non-preventable cases (n=28) from 365 adult general medicine patients who expired and underwent our institution’s mortality case review process. After excluding patients with a length of stay of more than one month, each case was reviewed by two blinded clinicians trained in our process and by an expert panel. Inter-rater reliability was assessed. We compared the frequency of DE contributing to death in both cohorts, as well as mean DPFs and e-triggers for DE positive and negative cases within each cohort. Results Twenty-seven (96.4%) preventable and 24 (85.7%) non-preventable cases underwent our review process. Inter-rater reliability was moderate between individual reviewers (Cohen’s kappa 0.41) and substantial with the expert panel (Cohen’s kappa 0.74). The frequency of DE contributing to death was significantly higher for the preventable compared to the non-preventable cohort (56% vs. 17%, OR 6.25 [1.68, 23.27], p<0.01). Mean DPFs and e-triggers were significantly and non-significantly higher for DE positive compared to DE negative cases in each cohort, respectively. Conclusions We observed substantial agreement among final consensus and expert panel reviews using our structured EHR case review process. DEs contributing to death associated with DPFs were identified in institutionally designated preventable and non-preventable cases. While e-triggers may be useful for discriminating DE positive from DE negative cases, larger studies are required for validation. Our approach has potential to augment institutional mortality case review processes with respect to DE surveillance.

Objectives Diagnostic errors – inaccurate or untimely diagnoses or failures to communicate diagnoses – are harmful and costly for patients and health systems. Diagnostic disparities occur when diagnostic errors are experienced at disproportionate rates by certain patient subgroups based, for example, on patients’ age, sex/gender, or race/ethnicity. We aimed to develop and test the feasibility of a human centered design workshop series that engages diverse stakeholders to develop solutions for mitigating diagnostic disparities. Methods We employed a series of human centered design workshops supplemented by semi-structured interviews and literature evidence scans. Co-creation sessions and rapid prototyping by patient, clinician, and researcher stakeholders were used to generate design challenges, solution concepts, and prototypes. Results A series of four workshops attended by 25 unique participants was convened in 2019–2021. Workshops generated eight design challenges, envisioned 29 solutions, and formulated principles for developing solutions in an equitable, patient-centered manner. Workshops further resulted in the conceptualization of 37 solutions for addressing diagnostic disparities and prototypes for two of the solutions. Participants agreed that the workshop processes were replicable and could be implemented in other settings to allow stakeholders to generate context-specific solutions. Conclusions The incorporation of human centered design through a series of workshops promises to be a productive way of engaging patient-researcher stakeholders to mitigate and prevent further exacerbation of diagnostic disparities. Healthcare stakeholders can apply human centered design principles to guide thinking about improving diagnostic performance and to center diverse patients’ needs and experiences when implementing quality and safety improvements.

Objectives The acquisition of clinical reasoning (CR) skills is essential for future healthcare providers as they advance through their education. There is growing consensus that CR skills should be longitudinally integrated into undergraduate curriculum for acquisition/application of these skills. However, only a minority of schools reported having CR focused teaching sessions, citing a lack of curricular time and faculty expertise as the largest barriers. We describe the design and implementation of this theme and report the effects of its early introduction in Phase One as measured by the Diagnostic Thinking Inventory (DTI). Methods The Carver College of Medicine developed and implemented a longitudinal four-year clinical reasoning theme (CRT) with a special emphasis on introducing concepts in the preclinical years (Phase One). Educational strategies used to implement the theme relied on following principles: 1) new skills are best acquired in context of application; 2) contextual learning stimulates transfer of knowledge; and 3) knowledge of pathophysiology is necessary but alone is not sufficient to develop CR skills. A patient-centered CR schema served as the framework for developing the theme. Specific focus areas, pedagogies and assessment strategies were established for each of the three phases. Results The cohort with CRT demonstrated a significant increase in total DTI score after theme implementation compared to the cohort without. Conclusions A formal 4-year longitudinal CR theme is feasible, allowing for integration of pathophysiology, social determinants of health, and clinical skills. Early introduction of CR concepts as assessed by DTI showed improvement in student reasoning skills post-intervention.

Objectives Idiosyncratic approaches to reasoning among teachers and limited reliable workplace-based assessment and feedback methods make teaching diagnostic reasoning challenging. The Assessment of Reasoning Tool (ART) was developed to fill this gap, but its utility and feasibility in providing feedback to residents has not been studied. We evaluated how the ART was used to assess, teach, and guide feedback on diagnostic reasoning to pediatric interns. Methods We used an integrated mixed-methods approach to evaluate how the ART facilitates the feedback process between clinical teachers and learners. We collected data from surveys of pediatric interns and interviews of hospital medicine faculty at Baylor College of Medicine from 2019 to 2020. Interns completed the survey each time they received feedback from their attending that was guided by the ART. The preliminary intern survey results informed the faculty interview questions. We integrated descriptive statistics of the survey with the thematic analysis of the transcribed interviews. Results Survey data (52 survey responses from 38 interns) and transcribed interviews (10 faculty) were analyzed. The ART framework provided a shared mental model which facilitated a feedback conversation. The ART-guided feedback was highly rated in terms of structure, content, and clarity in goal-setting while enabling new learning opportunities. Barriers to using the ART included limited time and inter-faculty variability of its use. Conclusions The ART facilitated effective and feasible faculty feedback to interns on their diagnostic reasoning skills.

Objectives SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) structure and host tropism have changed over time, and so has the involvement of throat structures. This infodemiological analysis is hence aimed at investigating the evolution of throat symptoms during the COVID-19 pandemic in the US. Methods We searched Google Trends using the medical search terms “ageusia”, “hoarseness”, “dysphonia”, “pharyngitis” and “laryngitis”, setting geographical location to “United States”, within the past 5 years. The weekly Google Trends score for these symptoms, which reflects their national Web popularity, was divided in three parts, as “pre-COVID” (July 2017 to February 2020), COVID-19 “pre-Omicron” (March 2020 to November 2021), and COVID-19 “Omicron” (December 2021 to July 2022), and then compared. Results The volume of searches for ageusia increased in the pre-Omicron period and remained significantly higher also during Omicron prevalence, though a significant decrease (∼30%) occurred with Omicron lineages compared to previous strains. The Google searches for hoarseness and dysphonia were relatively similar between the pre-COVID and pre-Omicron periods, but then significantly increased during Omicron predominance. The Google searches for pharyngitis and laryngitis decreased in the pre-Omicron period, but then considerably increased following Omicron emergence. Omicron endemic spread could be significantly and independently predicted by the volume of searches for pharyngitis, laryngitis and hoarseness. Conclusions The epidemiological burden of throat symptoms has considerably changed after Omicron emergence, with a lower likelihood of developing chemosensory dysfunctions and enhanced risk of throat involvement.

Objectives Chemiluminescence immunoassay (CLIA) automated assays (fourth-generation antigen test) for SARS-CoV-2 detection are promising because of their analytical productivity, but have lower sensitivity and specificity than rt-PCR assays. The authors of this paper evaluated a recent immunoassay implemented on Siemens Atellica IM, investigating how much this could affect the actual feasibility of this diagnostic during the pandemic. Methods From the three-day routine 134 positive and 241 negative swab samples by rt-PCR test were evaluated, selected as 1/3 positive – 2/3 negative. Results Using rt-PCR as gold standard, the specificity of immunoassay was 96.7%, while sensitivity was 68.0%. Sensitivity is inversely proportional to the viral load: 100% for cycles threshold (CT) values from 14 to 29, 95% until 30 CT, then 85, 74, 72, 68%, for 31–35 CT respectively. Conclusions Our study confirms the reliability of the fourth-generation antigen assay in recognizing negative samples. Conversely, sensitivity appears to be less reliable (68.0%) than reported in the literature. This could be due to a non-randomized study group: many swab samples were taken from patients with expected low viral load (hospitalized for COVID for more than 10–12 days or asymptomatic patients for epidemiological surveillance). The strong correlation of sensitivity and viral load could prove significant to track the infectiousness of infected people, as previous studies reported that a viral load of at least 10E6 copies of RNA/mL, corresponding to 25 CT, is the threshold of transmission of the disease.

Objectives Microribonucleic acids (microRNA/miRNA/miR-) are predicted to be useful in the early diagnosis, monitoring, and treatment of diabetic nephropathy (DN). We aimed to investigate the relationship of DN to miR-21-3p, miR-29a-3p, miR-29b-3p, miR-29c-3p, miR-126-3p, miR-129-1-3p, miR-137, miR-192-5p, miR-212-3p, and miR-320c. Methods There were 50 healthy controls and 100 patients with type 2 diabetes mellitus (T2DM). The diabetic patients were divided into three subgroups: normal to mildly increased (A1, n=51), moderately increased (A2, n=25), and severely increased (A3, n=24) albuminuria. The biochemical measurements were analysed using Roche Cobas 8000. The plasma miRNAs were analysed using RT-qPCR based on SYBR green chemistry. Results The relative expression of miR-21-3p was significantly lower in the (A3 p=0.005, 6.6-fold decrease) and DN (A1 + A3) (p=0.005, 6.6-fold decrease) groups compared to the controls. The relative expression of miR-192-5p was also significantly lower in the DN group (p=0.027, 2.4-fold decrease) compared to the controls. The area under curve value was 0.726 for miR-21-3p and 0.717 for miR-192-5p for distinguishing the DN group from the controls. Conclusions The decreased expressions of miR-21-3p and miR-192-5p are associated with the development of DN and may be potential biomarkers for the early diagnosis of DN.