Development and Application of Clinical Prediction Rules to Improve Decision Making in Physical Therapist Practice
Notice bibliographique
Résumé
Clinical prediction rules (CPRs) are tools designed to improve decision making in clinical practice by assisting practitioners in making a particular diagnosis, establishing a prognosis, or matching patients to optimal interventions based on a parsimonious subset of predictor variables from the history and physical examination.1,2 Clinical prediction rules have been developed to improve decision making for many conditions in medical practice, including the diagnosis of proximal deep vein thrombosis (DVT),3 strep throat,4 coronary artery disease,5 and pulmonary embolism.6 Clinical prediction rules also have been developed to assist in establishing a prognosis such as determining when to discontinue resuscitative efforts after cardiac arrest in the hospital,7 determining the likelihood of death within 4 years for people with coronary artery disease,7 identifying children who are at risk for developing urinary tract infections,8 and identifying the characteristics of patients who are likely to develop postoperative nausea and vomiting after anesthesia.9 Clinical prediction rules have recently been developed that can improve decision making in physical therapist practice. Examples include prediction rules to improve the accuracy of diagnosing ankle fractures (ie, “the Ottawa Ankle Rules”)10 and knee fractures (ie, “the Ottawa Knee Rules”)11 in people with acute injuries and to determine when to order radiographs in patients with neck trauma.12 Other prediction rules have been developed to diagnose patients with cervical radiculopathy13 and carpal tunnel syndrome.14 A CPR also has been developed to establish the prognosis of patients with neck pain following a rear-end motor vehicle accident.15 With increasing attention focused on the rising costs of health care, CPRs provide practitioners with powerful diagnostic information from the history and physical examination that may serve as an accurate decision-making surrogate for more expensive diagnostic tests. For example, the Ottawa Ankle Rules identify only those patients in which the probability of having a fracture is sufficiently large to warrant radiographic imaging, thus reducing costs and avoiding exposing patients to unnecessary radiation.16 Similarly, if the CPRs used to diagnose patients with cervical radiculopathy13 and carpal tunnel syndrome14 are eventually validated, the demand for electrodiagnostic testing may be reduced, potentially saving costs and avoiding the discomfort and anxiety associated with these procedures. In addition to their diagnostic utility, CPRs pertinent to physical therapist practice have recently been developed to assist with subgrouping patients into specific classifications that are useful in guiding management strategies. For example, CPRs have been developed to help practitioners match patients to optimal treatment approaches such as spinal manipulation17,18 and a lumbar stabilization exercise program.19 An advantage of CPRs is that they use the diagnostic properties of sensitivity, specificity, and positive and negative likelihood ratios (LR); thus, their interpretation can be readily applied to individual patients.1 Although helpful for guiding the early stages of treatment and assigning patients to a particular classification, they are not always useful for prescribing the exact treatment techniques to be used within the context of the patient’s assigned classification. Because CPRs are designed to improve decision making, it is important that they be developed and validated according to rigorous methodological standards. McGinn et al1 have suggested a 3-step process for developing and testing a CPR prior to widespread implementation of the rule in clinical practice. The purpose of this update is to describe the different steps involved in developing and validating CPRs and illustrate how CPRs can be used to improve decision making in physical therapist practice. Clinical prediction rules have the potential to improve outcomes, increase patient satisfaction, and decrease costs of care in physical therapist practice. The initial step in the development of a CPR involves creation of the rule (Fig. 1). Researchers and practitioners may initially brainstorm to develop a list of all possible factors that they believe have some predictive value for identifying the condition of interest. Ultimately, a reasonable list of predictors are selected for consideration based on clinical experience and previous research, which demonstrates that the factor or set of factors has some diagnostic or prognostic accuracy. Although it may be ideal to include every possible factor from the clinical examination to ensure that no possible predictor variables are overlooked, the researcher must weigh the benefits of including a complete set of potential predictor variables against the increase in sample size required for each additional variable under consideration. Some authors20,21 have recommended that 10 to 15 subjects should be enrolled into the study to identify one predictor variable. The sample size also must be judged in the context of the risks and benefits of decision making based on the rule and the prevalence of a particular phenomenon. For example, there may be significant consequences associated with the failure to identify a clinically relevant cervical spine injury in a patient who has sustained neck trauma or with the failure to identify the presence of an ankle fracture. These studies, therefore, tend to enroll thousands of patients to achieve sufficiently narrow confidence intervals so that practitioners can be virtually certain that application of the rule will not lead to an error in decision making. These injuries also are relatively rare in light of the total number of traumatic injuries; thus, larger sample sizes are necessary to observe a sufficient number of events on which to base the accuracy calculations. Steps in the development of a clinical prediction rule. Steps in the development of a clinical prediction rule. On the other hand, although failing to identify a patient likely to benefit from a specific treatment approach such as spinal manipulation may result in a less-than-optimal outcome or a delay in improvement, the patient is unlikely to have a serious complication. Furthermore, the pretest probability of achieving a successful outcome with spinal manipulation (ie, the probability associated with a successful outcome before considering the patient’s status on the rule) was 45%, which is considerably higher than the prevalence of a clinically relevant cervical spine injury or ankle fracture among individual patients with an acute injury. This also permits a smaller sample size because fewer cases are necessary to observe a sufficient number of events (ie, a successful outcome) to characterize the accuracy of decision making within an acceptable level of confidence. The development of CPRs, therefore, requires the researcher to consider the prevalence that a particular event will occur and then balance the benefits of achieving ever more narrower confidence intervals against the additional costs associated with recruiting an increasingly large sample size. Once the initial set of possible predictor variables is established, patients are examined to determine the presence or absence of each predictor variable at baseline. To minimize bias, it is essential that the examiner be blinded from knowing whether the patient actually has the condition of interest. For example, in the development of the spinal manipulation CPR, a variety of demographic, historical, and physical examination findings were considered.18 Patients with low back pain first completed several questionnaires consisting of self-report measures of pain and function. Factors such as the mechanism of injury, nature of current symptoms, distal extent of symptoms, and previous episodes of low back pain were considered as possible predictor variables. In addition, psychosocial considerations such as fear-avoidance beliefs and nonorganic signs and symptoms were considered. Physical examination findings that were considered to be potential predictor variables included hip and lumbar spine range of motion, lumbar spine mobility testing, and a variety of traditional landmark symmetry and provocation tests purported to identify dysfunction in the lumbopelvic region. After patients are examined at baseline for the presence of the possible predictor variables, a second examiner who is blinded to the results of the clinical examination should then establish whether the patient actually has the condition of interest according to a standardized and well-accepted reference criterion (ie, “gold standard” or “reference standard”). Reid et al22 suggested that an appropriate reference criterion is one that accurately represents the condition the diagnostic test is attempting to identify. For the spinal manipulation CPR, the purpose was to “diagnose” patients with low back pain who were likely to achieve a dramatic improvement from spinal manipulation after 1 week.18 The reference criterion, therefore, was based on response to a standardized manipulative intervention according to a predetermined clinically relevant cutoff score. In this case, patients who achieved at least 50% improvement on the Oswestry Low Back Pain Disability Questionnaire, a patient’s perceived level of disability, were considered to have achieved a successful outcome. Previous research23–25 has shown that 50% improvement on the Oswestry Low Back Pain Disability Questionnaire distinguishes between patients responding to manipulation versus those simply benefiting from the favorable natural history of low back pain. During development of the Ottawa Ankle Rules10 and Ottawa Knee Rules,11 radiographs were the reference criterion to determine whether an ankle or knee fracture was present. Neural conduction using electrodiagnostic testing based on well-established guidelines for the diagnosis of carpal tunnel syndrome and cervical radiculopathy was used as the reference criterion to determine which patients actually had the condition.13,14 The credibility and usefulness of the CPR that is eventually developed hinges upon the selection of an appropriate and clinically meaningful reference criterion. 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carpal tunnel syndrome is in on a pretest probability of that the patient may have carpal tunnel syndrome and a positive of when at least 4 of findings are the probability of having carpal tunnel syndrome to a positive of when all findings are the probability to An of the patient’s status on the therefore, can help the diagnostic process for determining whether the patient has carpal tunnel decision-making can be applied to the cervical radiculopathy CPR (Fig. a pretest probability of and a positive of for patients with at least of 4 findings the probability of having a cervical radiculopathy is to all 4 findings are a positive of the probability to increasing the level of confidence that the patient has a cervical advantage of CPRs is in addition to decision making based on the patient’s status on the the individual variables that the rule to be For example, the which is by the by the based on and is to be an of carpal was the useful test for carpal tunnel syndrome when the value The test by was the test for establishing a diagnosis of cervical With a negative to a negative test rules the presence of cervical of for Clinical of for Clinical McGinn et have an to assist practitioners in determining whether the CPR is appropriate for use in the decision-making Clinical prediction rules in which the rule has been not validated are as level For example, the carpal tunnel syndrome and cervical radiculopathy prediction rules to a level CPR, the first step in the development of the rule. are necessary before these rules can be recommended for widespread use in clinical practice. A level CPR is one that has only been validated in one narrow and thus should be used with among patients in a practice Clinical prediction rules be recommended for widespread implementation at least one large study in a of patients and practitioners has been in a variety of practice For example, the spinal manipulation CPR study with a variety of experience in different health care thus, the it as level on the This the confidence that the spinal manipulation CPR can be used in a of patients with low back pain to improve decision making and patient The study of the CPR to identify with conditions who have a proximal also is with a level increasing a confidence in the accuracy among patients with A level CPR to the level of and requires at least one study in a different results from an study in practice of care, and costs For example, practitioners can be that decision making based on the Ottawa Ankle Rules will not only be accurate in a variety of health care will also the at which ankle radiographs to be thus reducing health care reasonable to believe that for a level or level CPR should be sufficient to practice their a to be with the is a having a level CPR such as the Ottawa Ankle Rules not that it can be into clinical practice. and no in the use of ankle among who had been in the use of the Ottawa Ankle The for practitioners is to an to CPRs in a clinical are required to the individual factors in the CPR and how to patients with to each criterion, and they must in the context of the decision-making process to the accuracy of their CPRs that have many predictor variables may be for the to and in clinical practice. practitioners are that the CPR is to use and will improve costs or of care, implementation may be to to not to thus, efforts should be to implementation that standardized practice that are with the to the of each practice are Clinical prediction rules have the potential to improve outcomes, increase patient satisfaction, and decrease costs of care in physical therapist practice. can be useful tools to practitioners and to patients their diagnosis or development has to among health care that diagnostic tests such as radiographs or tests provide useful for decision making, information from the patient history is more and not as they also may be useful to increase the of clinical by to study more of
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,162 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».