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Jill Maddison, BVSc, DipVetClinStud, PhD, SFHEA, MRCVS, Royal Veterinary College, London, United Kingdom
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Undergraduate and continuing education tend to focus on acquisition of knowledge; however, knowledge is only useful if it can be accessed, formulated, and applied to the problem at hand. Thus, successful case assessment requires knowledge, understanding, and clinical reasoning.
Sheba, a 3-year-old spayed rottweiler located in New York, is presented with an acute history of melena and collapse overnight. She had vomited bile once a few hours prior, had been active and normal the preceding day, and had eaten well the preceding afternoon. On physical examination, she is overweight and weak with pale mucous membranes, a prolonged capillary refill time (>2 seconds), a heart rate of 160 bpm, and a normal rectal temperature. A systolic heart murmur (grade 2/6) is auscultated on the left-hand side. Her spleen appears large on abdominal palpation. She is up-to-date on vaccinations and parasite preventives and has not recently traveled.
Clinical reasoning is a complex process that varies widely depending on the clinician’s preferred thinking and learning style, past experiences and expertise, the clinical problem itself, and the context in which the problem is encountered. Clinical reasoning used by clinicians can be broadly classified as Type 1 (nonanalytic) or Type 2 (analytic). A blended approach or triangulation of both types (to cross-check clinical reasoning and diagnostic conclusions) is advocated for successful diagnostic decision-making.1
Nonanalytic reasoning, often referred to as pattern recognition, occurs quickly and subconsciously and primarily relies on the clinician accessing knowledge and patterns from past experiences that can be applied to the present case. Thus, limited previous case exposure may hinder pattern recognition in students and new graduates, veterinarians returning to practice after a prolonged break, or veterinarians changing their area of practice. Nonanalytic reasoning based on pattern recognition can be also be flawed if the clinician recognizes only a small number of salient factors in the case.
Use of pattern recognition as the primary mode of clinical reasoning works well for many common disorders and has the advantage of being quick and cost-effective, provided that the diagnosis is correct. Pattern recognition is also effective in cases for which:
Alternatively, pattern recognition as the primary clinical reasoning process can be problematic:
For an experienced clinician, the success of pattern recognition relies on a correct diagnosis for the previously observed pattern. In general practice, the clinician must often form a provisional diagnosis and make treatment decisions in the absence of complete knowledge or data and without confirming the diagnosis.2 These decisions will likely be reinforced by the presumption that the diagnosis was correct if the patient clinically improves with treatment.
Even experienced clinicians are vulnerable to bias (Table) in nonanalytic reasoning. Such bias is generally subconscious, although some authors suggest that an awareness of bias can help avoid such errors. Diagnostic error can involve a combination of biases. Cognitive skill errors (ie, processing biases) are reported to be a more common reason for diagnostic error as compared with errors caused by knowledge gaps.1 Overconfidence is believed to be a major factor contributing to diagnostic error and bias, even among specialists.3
In Sheba’s case, the range of diagnoses that will be suggested by veterinarians based on pattern recognition may include an acute GI disease, a bleeding disorder, acute cardiac failure, splenic torsion, splenic hemangiosarcoma, hypoadrenocorticism, and hemolytic anemia. All of these are feasible and all require different diagnostic and treatment strategies.
Routine diagnostic tests (eg, hematology, serum chemistry profile, urinalysis) can be useful and often essential in understanding a patient’s clinical condition. Relying on a minimum database to provide more information about the patient before clinical reasoning is engaged may be reasonable for some diseases but unhelpful for others. Serious, even life-threatening, disorders of the GI tract, neuromuscular system, pancreas (especially in cats), and heart rarely cause significant diagnostic changes in the routine hematologic and biochemical parameters measured in general practice. In addition, diagnostic tests are rarely 100% sensitive or specific. Using blood testing to screen for diagnoses can therefore be misleading, as the positive and negative predictive value of any test is influenced by the prevalence of a disorder in the population.
Abnormal results in an unwell patient can create confusion if not critically reviewed as an integral part of the clinical assessment of all data relevant to the patient and related to the presenting problem(s). Veterinarians may overestimate the information gained from laboratory and imaging results,1 especially if the fundamentals (ie, comprehensive history, thorough clinical examination) are bypassed in favor of tests. It is recommended to avoid performing a test if not looking for a specific disease, as results can be misleading. For example, total thyroxine and fecal panels are tests that are requested frequently but that may be misinterpreted.
For cases in which nonanalytic reasoning is not helpful, analytic reasoning is required. An analytic approach to clinical reasoning is also needed to double-check presumptive diagnoses that are based on pattern recognition.
In contrast to nonanalytic reasoning, analytic reasoning is reflective and systematic, permitting hypothesis formation and abstract reasoning.
Analytic reasoning is less prone to bias than nonanalytic reasoning2 but is limited by working memory capacity, unless strategies are developed to provide the clinician with a logical, methodical, and memorable process through which to problem-solve any case presentation.
In problem-based inductive reasoning, also described as logical clinical problem-solving, each significant clinicopathologic problem is assessed before being related to the patient’s other problems. Using this approach, the pathophysiologic basis and key questions for the most specific clinical signs the patient is exhibiting are considered before a pattern is sought. This ensures that the clinician’s mind remains more open to other diagnostic possibilities beyond the most obvious based on pattern recognition and thus helps prevent diagnostic bias.
The initial step in problem-based inductive reasoning is to clarify and articulate the patient’s clinical signs by constructing a problem list. Constructing a problem list (either mentally, orally, or in written form) helps make the clinical signs explicit to the clinician’s current level of understanding, transforms vague presenting information to specific problems, and helps the clinician determine the key clinical problems (ie, hard findings) versus the “background noise” (ie, soft findings). Most importantly, it helps prevent the clinician from overlooking less obvious, but nevertheless crucial, clinical signs and becoming overwhelmed with information. Incidental findings can mislead the clinician, particularly in older patients (eg, by focusing on the chronic diseases present instead of recognizing that it may be an acute disease that is responsible for the current clinical signs). Constructing and critically assessing a problem list can help prevent this. Problems should be prioritized, and those that are most specific and/or diagnostically useful can act as “diagnostic hooks.”4
Sheba’s problem list would include:
Each acute problem is important, and answering key questions related to each can provide important clues to guide diagnosis.
Problem-based inductive clinical reasoning provides steps to bridge the gap between the problem list and the list of differential diagnoses via a structured format. Once the problem list has been formulated, it can be used as the foundation for problem-based reasoning. After the key problems have been assessed as below, rather than listing every possible differential diagnosis for every problem on the problem list, a list of feasible differential diagnoses based on the problem list as a whole should be made.
The specific problems identified should be investigated through rigorous use of key steps:
This structured approach to defining and refining the problem and system in particular will help determine the appropriate questions to ask when obtaining the history. The owner responses may alert the clinician to pay particular attention to aspects of the physical examination, indicate the most appropriate diagnostic test(s) to use, and prepare the clinician intellectually to assess the results of the chosen tests.
When assessing a patient’s clinical signs, it is essential to define the problem as accurately as possible. Considering whether there is another clinical sign with which the problem could be confused is a vital first step, as failure to define the problem correctly can derail a clinical investigation that might otherwise have been relatively straightforward.
In Sheba’s case, melena in particular requires careful problem definition, as digested blood in the GI tract can be a result of either GI bleeding or swallowed blood (eg, from eating raw red meat, a bleeding lesion in the mouth or nasopharynx, coughing up then swallowing blood, licking a bleeding wound). It should be confirmed that the melena is due to GI bleeding by ruling out possible sources of ingested blood.
Once the problem is defined, the body system that is malfunctioning should be considered. For every clinical sign, there is a system(s) that must be involved or that “creates” the clinical sign. However, the most important question is how it is involved. The key specific questions are what system could be involved in causing this clinical sign and is it a primary (ie, structural) problem of a body system or a secondary (ie, functional) problem whereby the system involved in creating the particular clinical sign is secondarily affected in the pathophysiologic process. An alternative, although closely related, question for some problems is if the problem is local or systemic.
In Sheba’s case, the key questions related to system definition include:
The range of diagnoses to consider, diagnostic tools used, and potential treatment or management options for primary structural problems of a body system are often very different from those relevant to secondary functional problems of that system. Investigation of primary structural problems often involves imaging (eg, radiology, ultrasonography, advanced trans-sectional imaging, endoscopy, surgical exploration) and/or biopsy. Routine hematology, serum chemistry profile, and urinalysis are often of little value in confirming the diagnosis but can be helpful in assessing the consequences of the underlying pathology (eg, anemia from GI bleed, metabolic perturbations as a result of vomiting and diarrhea in primary GI disease).
In contrast, for secondary functional disorders, hematology and serum chemistry profile are often critical in reaching a diagnosis.
In Sheba’s case, the problem-based approach has clarified that there are several key questions that need to be answered:
For some cases, once the system and its involvement are defined, the location within the system may need to be determined. For all problems, once the system and its involvement are determined, the lesion should be defined (ie, the differential diagnosis list).
In Sheba’s case, the key findings include:
Helpful diagnostic tools would include a full hemogram and blood smear examination to assess RBC, WBC, and platelet morphology; a full coagulation profile; assessment for infectious diseases if in an endemic area; and abdominal imaging to check for abdominal hemorrhage and assess the liver and spleen.
Sheba’s final diagnosis is primary immune-mediated anemia and thrombocytopenia (ie, Evan’s Syndrome). She is treated successfully with corticosteroids and azathioprine.
As with all skills, it takes time to develop the knowledge base and mental discipline required for successful logical clinical problem-solving. However, once the logical clinical problem-solving approach is embedded (and, ideally, becomes part of the clinician’s nonanalytic reasoning), it can save time by quickly eliminating extraneous information and helping the clinician focus on the information that is truly important for patients and owners.
PCV = packed cell volume
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