Clinical Reasoning in Veterinary Practice. Группа авторов

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be able to help a little bit here as well), we hope that by the end of this book, we will have gone some way towards removing the common barriers to correct, quick and efficient diagnosis of medical cases and have made unravelling medical riddles fun rather than frustrating.

      Solving clinical cases

      When a patient presents with one or more clinical problems, there are various methods we can use to solve the case and formulate a list of differential diagnoses. One method involves pattern recognition – looking at the pattern of clinical signs and trying to match that pattern to known diagnoses. This is also referred to as developing an illness script. Another method can involve relying on blood tests to tell us what is wrong with the patient – also referred to as the minimum database. Or we can use problem‐based clinical reasoning. Often, we may use all three methods.

      Let’s consider three cases. Each of these will trigger thoughts and ideas about possible diagnoses depending on your knowledge and experience.

       Case 1: ‘Sundance’

      Sundance is a 17‐year‐old female (neutered) domestic short‐haired cat with a 1‐month history of increased appetite (polyphagia) and increased drinking (polydipsia). Obvious weight loss had been noted by owner over this period of time. Sundance has seemed more agitated and demanding of food and attention.

       Case 2: ‘Brutus’

      Brutus is a 10‐year‐old male neutered Dalmatian with a 3‐day history of vomiting bile and excessive urinating and drinking (polyuria/polydipsia) for 10 days. His appetite has been much reduced for about 10 days as well. On physical examination he was found to be depressed and dehydrated with no other significant abnormalities noted.

       Case 3: ‘Erroll’

      Erroll is a 4‐year‐old neutered male Burmese cat. He has a 2‐week history of intermittently vomiting bile‐stained material. Over the last 4–5 days he has become progressively anorexic and depressed. 24 hours prior to presentation he had started straining to urinate, and the urine was blood stained. No diarrhoea had been noted by the owners. His water intake was normal until the past 24 hours, when it may have been reduced.

      On physical examination he was noted to be very depressed and dehydrated. His rectal temperature was normal (38.1°C). Heart rate was elevated at 220 bpm. Mucous membrane colour was poor and the capillary refill time (CRT) was greater than 3 seconds. Abdominal palpation was unremarkable – the kidneys felt normal and were not painful. The bladder contained some urine but felt normal and could be easily expressed.

      So – can we solve all of these cases in the same way? Do we need to? What are the challenges? Let’s consider the tools we use to clinically reason.

      Pattern recognition

      Pattern recognition involves trying to remember all diseases that fit the ‘pattern’ of clinical signs/pathological abnormalities that the animal presents with. This may be relatively simple (but can also lead to errors of omission) and works best:

       For common disorders with typical presentations

       If a disorder has a unique pattern of clinical signs

       When all clinical signs have been recognised and considered, and the differential list is not just based on one cardinal clinical sign and the signalment of the patient presented

       If there are only a few diagnostic possibilities that areeasily remembered orcan easily be ruled in or out by routine tests

       If the vet has extensive experience, is well read and up‐to‐date, reflects on all of the diagnoses made regularly and critically and has an excellent memory.

      Pattern recognition works well for many common disorders and has the advantage of being quick and cost‐effective…provided the diagnosis is correct. The vet appears competent to the client because the vet has acted decisively and confidently…provided the diagnosis is correct.

      An example of a case where pattern recognition will invariably be used by most vets and will be successful (most of the time) is Sundance. The differential diagnoses for the pattern of clinical signs – weight loss despite polyphagia associated with polydipsia, altered behaviour and tachycardia – are very limited with hyperthyroidism being the explanation in the vast majority of cases. Score 10 out of 10 for pattern recognition!

      However, pattern recognition can be flawed and unsatisfactory when the clinician is inexperienced (and therefore has seen very few patterns) or only considers or recognises a small number of factors (and is not aware that this process is mainly driven by unconscious processes that might need to be reflected upon if they fail). Or even if the clinician is experienced, it can be flawed for uncommon diseases or common diseases presenting atypically, when the patient is exhibiting multiple clinical signs that are not immediately recognisable as a specific disease, or if the pattern of clinical signs is suggestive of certain disorders but not specific for them.

      The pattern of clinical signs that Brutus is showing has a much larger range of causes, but it is likely that an experienced veterinarian will recognise several possibilities though often not all. An inexperienced clinician will consider fewer potential differentials.

      For Brutus the liver enzymes were substantially increased, hypercalcaemia was noted and the final diagnosis was hypercalcaemia associated with hepatic lymphoma (confirmed on ultrasound‐guided biopsy).

      The pattern of clinical signs that Erroll is showing are just downright weird, involving different body systems over a period of time and with no ‘obvious’ single explanation for all of the signs even for very experienced clinicians. His bloodwork only showed an inflammatory leukogram. The final diagnosis was a pancreatic abscess and peritonitis from which E. coli was cultured and a urinary tract infection – from which E. coli was cultured.


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Availability bias A tendency to favour a diagnosis because of a case the clinician has seen recently.
Anchoring bias Where a prior diagnosis is favoured but is misleading. The clinician persists with the initial diagnosis and is unwilling to change his/her mind.
Framing bias Features that do not fit with the favoured diagnosis are ignored.