Band 9, Heft 1

Objectives The current literature on diagnosis of periprosthetic joint infection (PJI) provides controversial evidence on the diagnostic accuracy of D-dimer. Therefore, this critical literature search and meta-analysis was aimed to summarize the diagnostic accuracy of D-dimer for diagnosing PJI. Content We searched MEDLINE, Scopus, and Web of Science, for studies on D-dimer for diagnosing PJI, according to the PRISMA flowchart. QUADAS was used for assessing study quality. Sensitivity, specificity, positive (PLR) and negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were analyzed using bivariate diagnostic random-effects model. The area under the receiver-operating curve (AUC-ROC) was calculated. Subgroup analysis and univariate meta-regression were carried out for detecting potential sources of heterogeneity. Summary We included 12 articles, totaling 1,818 patients (539 with PJI). The pooled sensitivity and specificity of D-dimer for diagnosing PJI were 0.739 (95% CI: 0.616–0.833) and 0.785 (95% CI: 0.679–0.863). The pooled PLR, NLR, DOR were 3.359 (95% CI, 2.340–4.821), 0.295 (95% CI, 0.180–0.484), and 11.787 (95% CI, 5.785–24.018). The cumulative ROC plot displayed an AUC of 0.688 (95% CI, 0.663–0.713; p<0.001). No threshold effects could be observed. The type of blood sample was identified as possible source of heterogeneity for DOR (p=0.01). Outlook Evidence emerged from this meta-analysis suggests that D-dimer displays sufficient diagnostic accuracy to rule out PJI. The type of blood sample (plasma vs. serum) and the study design could influence the results in terms of DOR and sensitivity. However, further perspective studies would be needed to validate its potential diagnostic usefulness.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prosecuting, with nearly 300 million infections and over 5.3 million deaths recorded so far since the origin of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic at the end of the year 2019. The fight against this new highly virulent beta coronavirus appears one of the most strenuous and long challenges that humanity has ever faced, since a definitive treatment has not been identified so far. The adoption of potentially useful physical preventive measures such as lockdowns, social distancing and face masking seems only partially effective for mitigating viral spread, though efficacy and continuation of such measures on the long term is questionable, due to many social and economic reasons. Many COVID-19 vaccines have been developed and are now widely used, though their effectiveness is challenged by several aspects such as low uptake and limited efficacy in some specific populations, as well as by continuous emergence of new mutations in the SARS-CoV-2 genome, accompanying the origin and spread of new variants, which in turn may contribute to further decrease the effectiveness of current vaccines and treatments. This article is hence aimed to provide an updated picture of SARS-CoV-2 variants and mutations that have emerged from November 2019 to present time (i.e., early December 2021).

Traumatic brain injuries (TBIs) and sports-related concussions (SRCs) are the leading causes of hospitalization and death in subjects <45 years old in the USA and Europe. Some biomarkers (BMs) have been used to reduce unnecessary cranial computed tomography (CCT). In recent years, the astroglial S100 calcium-binding B protein (S100B) has prevented approximately 30% of unnecessary CCTs. Glial fibrillary acidic protein (GFAP) has also been studied in direct comparison with S100B. The aim of our cumulative meta-analysis (cMA) is to compare – in the context of hospital emergency departments or SRC conditions – the differences in diagnostic accuracy (DA), sensitivity (Se) and specificity (Sp) of GFAP and S100B. The main cMA inclusion criterion was the assessment of both BMs in the included subjects since 2010, with blood samples drawn 1–30 h from the suspected TBI or SRC. The risk-of-bias (RoB) score was determined, and both the publication bias (with the Begg, Egger and Duval trim-and-fill tests) and sensitivity (with the box-and-whiskers plot) were analyzed for outliers. Seven studies with 899 subjects and nine observations (samples) were included. The diagnostic odds ratios (dORs) with their prediction intervals (PIs), Se and Sp (analyzed with a hierarchical model to respect the binomial data structure) were assessed, and a random-effects MA and a cMA of the difference in the BMs dOR natural logarithms (logOR(G-S)) between the BMs were performed. The cMA of dOR(G-S) was significant (5.78 (CI 2–16.6)) probably preventing approximately 50% of unnecessary CCTs. Further work is needed to standardize and harmonize GFAP laboratory methods.

Diagnostic errors are an internationally recognized patient safety concern, and leading causes are faulty data gathering and faulty information processing. Obtaining a full and accurate history from the patient is the foundation for timely and accurate diagnosis. A key concept underlying ideal history acquisition is “history clarification,” meaning that the history is clarified to be depicted as clearly as a video, with the chronology being accurately reproduced. A novel approach is presented to improve history-taking, involving six dimensions: Courtesy, Control, Compassion, Curiosity, Clear mind, and Concentration, the ‘6 C’s’. I report a case that illustrates how the 6C approach can improve diagnosis, especially in relation to artificial intelligence tools that assist with differential diagnosis.

Telemedicine has seen a rapid expansion lately, with virtual visits ushering in telediagnosis. Given the shift in the interpersonal and technical aspects of communications in a virtual visit, it is prudent to understand its effect on the patient-provider relationships. A range of interpersonal and communication skills can be utilized during telemedicine consultations in establishing relationships, and reaching a diagnosis. We propose a construct of “webside manner,” a structured approach to ensure the core elements of bedside etiquette are translated into the virtual encounter. This approach entails the totality of any interpersonal exchange on a virtual platform, to ensure a clinician’s presence, empathy and compassion is translated through this medium.

Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

Objectives To improve diagnostic ability, educators should employ multifocal strategies. One promising strategy is self-explanation, the purposeful technique of generating self-directed explanations during problem-solving. Students self-explain information in ways that range from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. In a previous phase of research, unique ways that family nurse practitioner (NP) students self-explain during diagnostic reasoning were identified and described. This study aims to (a) explore relationships between ways of self-explaining and diagnostic accuracy levels and (b) compare differences between students of varying expertise in terms of ways of self-explaining and diagnostic accuracy levels. Identifying high-quality diagnostic reasoning self-explanation types may facilitate development of more refined self-explanation educational strategies. Methods Thirty-seven family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern university diagnosed three written case studies while self-explaining. During the quantitative phase of a content analysis, associational and comparative data analysis techniques were applied. Results Expert students voiced significantly more clinical and biological inference self-explanations than did novice students. Diagnostic accuracy scores were significantly associated with biological inference scores. Clinical and biological inference scores accounted for 27% of the variance in diagnostic accuracy scores, with biological inference scores significantly influencing diagnostic accuracy scores. Conclusions Not only were biologically focused self-explanations associated with diagnostic accuracy, but also their spoken frequency influenced levels of diagnostic accuracy. Educational curricula should support students to view patient presentations in terms of underlying biology from the onset of their education.

Objectives Clinical reasoning skills are essential for sound medical decision-making. Though many have suggested that clinical reasoning instruction should begin in pre-clerkship curricula, neither pre-clerkship clinical skills director perspectives nor extent of instruction is known. This survey study serves as part of a needs assessment for United States medical school pre-clerkship clinical reasoning curricula. Methods United States medical school pre-clerkship clinical skills course directors were surveyed about perceived importance of formal instruction on clinical reasoning concepts, inclusion of these concepts in the curricula, barriers to instruction, and familiarity with clerkship curricula. Results were analyzed using descriptive and analytic statistics. Narrative comments were analyzed qualitatively for themes. Results Of 148 directors surveyed, 102 (69%) participated and 89 (60%) completed all closed-ended items. Each clinical reasoning concept was identified as somewhat to extremely important to include in pre-clerkship curricula by 90–99% of respondents. Pre-clerkship curricula included variable degrees of formal instruction for concepts, though most respondents rated their inclusion as moderate or extensive. Perceived importance of teaching most concepts moderately correlated with the degree of inclusion in the curriculum (Spearman’s rho 0.39–0.44). Curricular time constraints and lack of faculty with skills to teach these concepts were the most frequently cited barriers to instruction. Respondents indicated being somewhat 57% (n=54) to extremely 29% (n=27) familiar with clerkship curricula at their institutions. Conclusions This study is the first to examine pre-clerkship clinical skills course director perspectives about clinical reasoning instruction and extent of its inclusion in their curricula.

Objectives In 2015, the National Academy of Medicine IOM estimated that 12 million patients were misdiagnosed annually. This suggests that despite prolonged training in medical school and residency there remains a need to improve diagnostic reasoning education. This study evaluates a new approach. Methods A total of 285 medical students were enrolled in this 8 center, IRB approved trial. Students were randomized to receive training in either abdominal pain (AP) or loss of consciousness (LOC). Baseline diagnostic accuracy of the two different symptoms was assessed by completing a multiple-choice question (MCQ) examination and virtual patient encounters. Following a structured educational intervention, including a lecture on the diagnostic approach to that symptom and three virtual patient practice cases, each student was re-assessed. Results The change in diagnostic accuracy on virtual patient encounters was compared between (1) baseline and post intervention and (2) post intervention students trained in the prescribed symptom vs. the alternate symptom (controls). The completeness of the student’s differential diagnosis was also compared. Comparison of proportions were conducted using χ 2 -tests. Mixed-effects regressions were used to examine differences accounting for case and repeated measures. Compared with baseline, both the AP and LOC groups had marked post-intervention improvements in obtaining a correct final diagnosis; a 27% absolute improvement in the AP group (p<0.001) and a 32% absolute improvement in the LOC group (p<0.001). Compared with controls (the groups trained in the alternate symptoms), the rate of correct diagnoses increased by 13% but was not statistically significant (p=0.132). The completeness and efficiency of the differential diagnoses increased by 16% (β=0.37, p<0.001) and 17% respectively (β=0.45, p<0.001). Conclusions The study showed that a virtual patient platform combined with a diagnostic reasoning framework could be used for education and diagnostic assessment and improved correct diagnosis compared with baseline performance in a simulated platform.

Objectives We describe an approach for analyzing failures in diagnostic processes in a small, enriched cohort of general medicine patients who expired during hospitalization and experienced medical error. Our objective was to delineate a systematic strategy for identifying frequent and significant failures in the diagnostic process to inform strategies for preventing adverse events due to diagnostic error. Methods Two clinicians independently reviewed detailed records of purposively sampled cases identified from established institutional case review forums and assessed the likelihood of diagnostic error using the Safer Dx instrument. Each reviewer used the modified Diagnostic Error Evaluation and Research (DEER) taxonomy, revised for acute care (41 possible failure points across six process dimensions), to characterize the frequency of failure points (FPs) and significant FPs in the diagnostic process. Results Of 166 cases with medical error, 16 were sampled: 13 (81.3%) had one or more diagnostic error(s), and a total of 113 FPs and 30 significant FPs were identified. A majority of significant FPs (63.3%) occurred in “Diagnostic Information and Patient Follow-up” and “Patient and Provider Encounter and Initial Assessment” process dimensions. Fourteen (87.5%) cases had a significant FP in at least one of these dimensions. Conclusions Failures in the diagnostic process occurred across multiple dimensions in our purposively sampled cohort. A systematic analytic approach incorporating the modified DEER taxonomy, revised for acute care, offered critical insights into key failures in the diagnostic process that could serve as potential targets for preventative interventions.

Objectives Participation in case-based diagnostic reasoning (DR) conferences has previously been limited to those who can attend in-person. Technological advances have enabled these conferences to migrate to virtual platforms, creating an opportunity to improve access and promote learner participation. We describe the design and evaluation of virtual morning report (VMR), a novel case-based DR conference that aimed to expand access to these conferences, leverage a virtual platform to create new opportunities for learner participation, and improve learner confidence in performing DR. Methods VMR took place on a videoconferencing platform. Participants included health professions students, post-graduate trainees, and practitioners. In designing VMR, we adapted concepts from the experience-based model of learning to design opportunities for learner participation. Teaching strategies were informed by information-processing and situativity theories. We evaluated learner experiences in VMR using a survey with open and closed-ended questions. Survey items focused on accessing case-based teaching conferences outside of VMR, participant perceptions of the educational value of VMR, and VMR’s impact on participants’ confidence in performing DR. We used thematic analysis to manually code open-ended responses and identify themes. Results 203 participants (30.2%) completed the survey. 141 respondents (69.5%) reported they did not otherwise have access to a DR conference. The majority of participants reported increased confidence performing DR. Respondents highlighted that VMR supplemented their education, created a supportive learning environment, and offered a sense of community. Conclusions VMR can expand access to DR education, create new opportunities for learner participation, and improve learner confidence in performing DR.

Objectives Isolated dizziness is a challenging stroke presentation in the emergency department, but little is known about this problem in other clinical settings. We sought to compare stroke hospitalizations after treat-and-release clinic visits for purportedly “benign dizziness” between general and specialty care settings. Methods This was a population-based retrospective cohort study from a national database. We included clinic patients with a first incident treat-and-release visit diagnosis of non-specific dizziness/vertigo or a peripheral vestibular disorder (ICD-9-CM 780.4 or 386.x [not 386.2]). We compared general care (internal medicine, family medicine) vs. specialty care (neurology, otolaryngology) providers. We used propensity scores to control for baseline stroke risk differences unrelated to dizziness diagnosis. We measured excess (observed>expected) stroke hospitalizations in the first 30 d (i.e., missed strokes associated with an adverse event). Results We analyzed 144,355 patients discharged with “benign dizziness” (n=117,117 diagnosed in general care; n=27,238 in specialty care). After propensity score matching, patients in both groups were at higher risk of stroke in the first 30 d (rate difference per 10,000 treat-and-release visits for “benign dizziness” 24.9 [95% CI 18.6–31.2] in general care and 10.6 [95% CI 6.3–14.9] in specialty care). Short-term stroke risk was higher in general care than specialty care (relative risk, RR 2.2, 95% CI 1.5–3.2) while the long-term risk was not significantly different (RR 1.3, 95% CI 0.9–1.9), indicating higher misdiagnosis-related harms among dizzy patients who initially presented to generalists after adequate propensity matching. Conclusions Missed stroke-related harms in general care were roughly twice that in specialty care. Solutions are needed to address this care gap.

Objectives Published discrepancy rates between emergency department (ED) and hospital discharge (HD) diagnoses vary widely (from 6.5 to 75.6%). The goal of this study was to determine the extent of diagnostic discrepancy and its impact on length of hospital stay (LOS), up-triage to the intensive care unit (ICU) and in-hospital mortality. Methods A retrospective chart review of adult patients admitted from the ED to a hospitalist service at a tertiary hospital was performed. The ED and HD diagnoses were compared and classified as concordant, discordant, or symptom diagnoses according to predefined criteria. Logistic regression analysis was conducted to examine the associations of diagnostic discordance with in-hospital mortality and up-triage to the ICU. A linear regression model was used for the length of stay. Results Of the 636 patients whose records were reviewed, 418 (217 [51.9%] women, with a mean age of 64.1 years) were included. Overall, 318 patients (76%) had concordant diagnoses, while 91 (21.77%) had discordant diagnoses. Only 9 patients (2.15%) had symptom diagnoses. A discordant diagnosis was associated with increased mortality (OR: 3.64; 95% CI: 1.026–12.91; p=0.045) and up-triage to the ICU (OR: 5.51; 95% CI: 2.43–12.5; p<0.001). The median LOS was significantly greater for patients with discordant diagnoses (7 days) than for those with concordant diagnoses (4.7 days) (p=0.004). Symptom diagnosis did not affect the mortality or ICU up-triage. Conclusions One in five hospitalized patients had discordant HD and admission diagnoses. This diagnostic discrepancy was associated with significant impacts on patient morbidity and mortality.

Objectives The Next Generation Sequencing (NGS) based mutational study of hereditary cancer genes is crucial to design tailored prevention strategies in subjects with different hereditary cancer risk. The ease of amplicon-based NGS library construction protocols contrasts with the greater uniformity of enrichment provided by capture-based protocols and so with greater chances for detecting larger genomic rearrangements and copy-number variations. Capture-based protocols, however, are characterized by a higher level of complexity of sample handling, extremely susceptible to human bias. Robotics platforms may definitely help dealing with these limits, reducing hands-on time, limiting random errors and guaranteeing process standardization. Methods We implemented the automation of the CE-IVD SOPHiA Hereditary Cancer Solution™ (HCS) libraries preparation workflow by SOPHiA GENETICS on the Hamilton’s STARlet platform. We present the comparison of results between this automated approach, used for more than 1,000 DNA patients’ samples, and the performances of the manual protocol evaluated by SOPHiA GENETICS onto 240 samples summarized in their HCS evaluation study. Results We demonstrate that this automated workflow achieved the same expected goals of manual setup in terms of coverages and reads uniformity, with extremely lower standard deviations among samples considering the sequencing reads mapped onto the regions of interest. Conclusions This automated solution offers same reliable and affordable NGS data, but with the essential advantages of a flexible, automated and integrated framework, minimizing possible human errors and depicting a laboratory’s walk-away scenario.

Objectives The relationship between diagnostic intensity and quality of care has not been well-characterized at the hospital level. We performed an exploratory analysis to better delineate this relationship using a hospital-level diagnostic overuse index and accepted hospital quality metrics (readmissions and mortality). Methods We previously developed and published a hospital-level diagnostic overuse index. A hospital’s overuse index value (which ranges from 0 to 0.986, with larger numbers indicating more overuse) was our predictor variable of interest. The outcome variables were excess readmission ratios and mortality rates for common medical conditions, which CMS publicly reports. The model controlled for Elixhauser comorbidity score, hospital bed size, hospital teaching status, and random effects that vary by state. Results We did not find a statistically significant relationship between our overuse index and the quality measures we evaluated. Conclusions The lack of a significant relationship between diagnostic intensity and quality, at least as measured by our overuse index and the tested quality metrics, suggests that well-targeted efforts to reduce diagnostic overuse in hospitals may not adversely impact quality of care.

Objectives Defects in human cognition commonly result in clinical reasoning failures that can lead to diagnostic errors. Case presentation A 43-year-old female was brought to the emergency department with 4–5 days of confusion, disequilibrium resulting in several falls, and hallucinations. Further investigation revealed tachycardia, diaphoresis, mydriatic pupils, incomprehensible speech and she was seen picking at the air. Given multiple recent medication changes, there was initial concern for serotonin syndrome vs. an anticholinergic toxidrome. She then developed a fever, marked leukocytosis, and worsening encephalopathy. She underwent lumbar puncture and aspiration of an identified left ankle effusion. Methicillin sensitive staph aureus (MSSA) grew from blood, joint, and cerebrospinal fluid cultures within 18 h. She improved with antibiotics and incision, drainage, and washout of her ankle by orthopedic surgery. Conclusions Through integrated commentary on the diagnostic reasoning process from clinical reasoning experts, this case underscores how multiple cognitive biases can cascade sequentially, skewing clinical reasoning toward erroneous conclusions and driving potentially inappropriate testing and treatment. A fishbone diagram is provided to visually demonstrate the major factors that contributed to the diagnostic error. A case discussant describes the importance of structured reflection, a tool to promote metacognitive analysis, and the application of knowledge organization tools such as illness scripts to navigate these cognitive biases.

Objectives Our discussant’s thoughtful consideration of the patient’s case allows for review of three maxims of medicine: Occam’s razor (the simplest diagnosis is the most likely to be correct), Hickam’s dictum (multiple disease entities are more likely than one), and Crabtree’s bludgeon (the tendency to make data fit to an explanation we hold dear). Case presentation A 66-year-old woman with a history of hypertension presented to our hospital one day after arrival to the United States from Guinea with chronic daily vomiting, unintentional weight loss and progressive shoulder pain. Her labs are notable for renal failure, nephrotic range proteinuria and normocytic anemia while her shoulder X-ray shows osseous resorption in the lateral right clavicle. Multiple myeloma became the team’s working diagnosis; however, a subsequent shoulder biopsy was consistent with follicular thyroid carcinoma. Imaging suggested the patient’s renal failure was more likely a result of a chronic, unrelated process. Conclusions It is tempting to bludgeon diagnostic possibilities into Occam’s razor. Presumption that a patient’s signs and symptoms are connected by one disease process often puts us at a cognitive advantage. However, atypical presentations, multiple disease processes, and unique populations often lend themselves more to Hickam’s dictum than to Occam’s razor. Diagnostic aids include performing a metacognitive checklist, engaging analytic thinking, and acknowledging the imperfections of these axioms.