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PubMed1990

The utility of routine screening of patients with uveitis for systemic lupus erythematosus or tuberculosis. A Bayesian analysis.

The indications for many laboratory tests in patients with uveitis are controversial. Bayes' theorem allows a mathematical approach to the assessment of the utility of a laboratory test based on the sensitivity of the test, the specificity of the test, and the pretest likelihood that the disease the test is intended to identify is present. We have utilized Bayes' theorem to assess the utility of routine antinuclear antibody and purified protein derivative testing in patients with uveitis. Based on published data about the sensitivity and specificity of each of these tests, as well as the prevalence of systemic lupus erythematosus and tuberculosis among patients with uveitis, we calculated that a patient with uveitis and a positive antinuclear antibody test result has less than a 1% chance of having systemic lupus erythematosus and that a patient with uveitis and a positive purified protein derivative test result has a 1% likelihood of having tuberculosis. These low probabilities mean that neither test is useful in the routine evaluation of patients with uveitis, and indiscriminate use may lead to improper diagnosis, increased costs, and, occasionally, inappropriate therapy.

Adult↗
PubMed1992

Bayesian analysis supports use of ambulatory blood pressure monitors for screening.

To assess whether there is a role for ambulatory blood pressure monitoring (ABPM) in screening for hypertension, we conducted Bayesian analysis of office blood pressure (OBP) as a diagnostic "test" in populations with different prior probabilities (PP) of hypertension. OBP was considered a positive test if systolic blood pressure was greater than 140 mm Hg or diastolic pressure was greater than 90 mm Hg. Chosen daytime ABPM cutoffs for a "gold standard" diagnosis of hypertension were systolic pressure of 139 and diastolic pressure of 88 mm Hg. Sensitivity and specificity of OBP were determined in 72 patients with established hypertension (PP = 1). After 3 weeks off therapy, OBP was 168 +/- 3/101 +/- 1 and ABPM was 151 +/- 2/94 +/- 1 mm Hg. Systolic ABPM was in the normotensive range in 17 patients and diastolic in 15 patients. OBP was falsely positive in 14 and 15 of these patients, respectively. Thus, sensitivity and specificity of OBP were 0.8909 and 0.1765 (systolic) and 0.9825 and 0 (diastolic). These data cannot be extrapolated to populations with lower PPs for use of Bayes' theorem. Hence, we calculated sensitivity and specificity for PP = 0 from published series of ABPM in normotensive subjects and used our measurements and these calculations in arithmetic interpolations for populations with PP 0.1-0.9. Sigmoid relations between PP and predictability of hypertension by a positive OBP were disclosed by use of Bayes' theorem. Their best-fit cubic polynomials predict that an elevated OBP will misdiagnose hypertension 46-50% of the time in a general population (PP = 0.2) but only 8-9% in a specialty practice (PP = 0.9).(ABSTRACT TRUNCATED AT 250 WORDS)

Bayes Theorem↗
PubMed2006

Imputation of individual cancer cases to occupational causes.

OBJECTIVES: Many potential occupational causes of cancer have been documented. Imputation of an individual cancer to occupational or other causes is, however, difficult. A method based on the Bayes theorem is proposed for assessing causal relationships at the individual level. METHODS: Causality assessment, dealing with four types of persons defined by exposure and the occurrence of cancer, was linked with imputation, only dealing with persons who have cancer and were exposed. Imputation was then formulated using the Bayes theorem, relating epidemiologic information regarding causes, a patient's exposure history, and the posterior odds that the cancer was caused by a suspected occupational exposure. Data needed to apply a Bayesian method were defined in terms of relative risks, proportion of people exposed in populations, and the frequency of a positive relevant characteristic for persons without cancer. A relevant characteristic was defined using a formal consensus between experts. The method was then illustrated with cases of mesothelioma and lung cancer in possible relation to asbestos. RESULTS: Experts defined the relevant characteristics as being qualification of occupational exposure, intensity of exposure, latency, disease characteristics, and presence of causal agent in the body. Application to mesothelioma and lung cancer cases illustrated the potential usefulness of the method. CONCLUSIONS: The importance of occupational exposure in the formulation of imputation underscores the need for available and reliable data sources on occupational exposures. The proposed method could become a powerful tool for the expert assessment of causes of cancer cases, provided data become available in individual files and the literature.

Aged↗
PubMed2004

Bayesian classification of myocardial excitation abnormality using magnetocardiogram maps for mass screening.

We propose a novel classification method based on the Bayes rule to utilize the magnetocardiogram (MCG) in noninvasive mass screening. The cardiac excitation is directly tracked by maps of the MCG field generated by myocardial excitation current through the excited wave front. To adopt the characteristics of the excited wave fronts as a parameter for the Bayes theorem, we developed a parameterization procedure that consists of a two-dimensional wavelet approximation and a cluster analysis of magnetic field maps. With the parameter determined by this procedure, the probability of a subject to belong to a disease group or to the normal group is estimated by the Bayes theorem. The subject is classified into the group of the highest probability. We applied the proposed method to ST-T period of MCG data of 6 old myocardial infarction (OMI) patients and 15 normal controls. The method showed sensitivity of 83%; specificity, 100%; positive predictive value, 100%; and negative predictive value, 94% in the classification of OMI patients and normal controls. The processing time is less than 5 seconds per one subject. It suggests a possible application of the proposed method in mass screening of abnormal MCG patterns.

Bayes Theorem↗
PubMed1989

Importance of epidemiology and biostatistics in deciding clinical strategies for using diagnostic tests: a simplified approach using examples from coronary artery disease.

The explosion of costly new medical diagnostic technologies demands a common sense approach to help physicians decide appropriate indications and strategies for use of these tests. This simple, nonmathematical review focuses on the assessment of coronary artery disease, but the approach can be generalized to other medical problems. This clinical approach to diagnostic testing strategies is based on seven sequential questions: 1. What is the clinical probability that this patient has a specific disease characteristic based on clinical data? 2. What is the overall objective for management of this patient based on the overall status of the patient? 3. Most importantly, what specific questions need to be answered about the patient's condition before the physician can recommend the most appropriate management (e.g., whether the patient has coronary disease, whether an anatomic lesion is functionally significant, whether a myocardial region is reversibly ischemic or irreversibly infarcted, whether a particular therapy has had good or bad effects or what is the patient's prognosis)? The key point is for the physician to formulate a specific clinical question about the patient before the test. 4. The physician must then ask how well does the test answer the particular clinical question about the patient. Here the physician needs to understand the sensitivity and specificity of the test, especially because they are influenced by various clinical biases. 5. Next, the physician must ask how to interpret the reliability of a positive or negative test result in the individual patient. This requires understanding predictive value and predictive error of a given result and how they are influenced by the clinical data as described by Bayes' theorem. 6. Next, the physician must ask what further tests or therapies will be recommended for the patient. The physician can estimate in advance how different test results would alter management plans and he can then allow this estimate to help determine indications for the test. There is some controversy concerning whether to use Bayes' theorem or multivariate analysis to estimate the final probability of a disease characteristic. 7. Finally, in this era of quality assurance, professional review and cost containment, it behooves each physician to ask whether the data provided by the particular tests were worth the cost, inconvenience and risk for that particular patient.

Bayes Theorem↗
PubMed1980

Application of conditional probability analysis to the clinical diagnosis of coronary artery disease.

Analysis of multiple noninvasive tests offers the promise of more accurate diagnosis of coronary artery disease, but discordant test responses can occur frequently and, when observed, result in diagnostic uncertainty. Accordingly, 43 patients undergoing diagnostic coronary angiography were evaluated by noninvasive testing and the results subjected to analysis using Bayes' theorem of conditional probability. The procedures used included electrocardiographic stress testing for detection of exercise-induced ST segment depression, cardiokymographic stress testing for detection of exercise-induced precordial dyskinesis, myocardial perfusion scintigraphy for detection of exercise-induced relative regional hypoperfusion, and cardiac fluoroscopy for detection of coronary artery calcification. The probability for coronary artery disease was estimated by Bayes' theorem from each patient's age, sex, and symptom classification, and from the observed test responses. This analysis revealed a significant linear correlation between the predicted probability for coronary artery disease and the observed prevalence of angiographic disease over the entire range of probability from 0 to 100% (P less than 0.001 by linear regression). The 12 patients without angiographic disease had a mean posttest likelihood of only 7.0 +/- 2.6% despite the fact that 13 of the 60 historical and test responses were falsely "positive." In contrast, the mean posttest likelihood was 94.1 +/- 2.8% in the 31 patients with angiographic coronary artery disease, although 45 of the 155 historical and test responses were falsely "negative." In 8 of the 12 normal patients, the final posttest likelihood was under 10% and in 26 of the 31 coronary artery disease patients, it was over 90%. These estimates also correlated well with the pooled clinical judgment of five experienced cardiologists (P less than 0.001 by linear regression). The observed change in probability for disease for each of the 15 different test combinations correlated with their information content predicted according to Shannon's theorem (P less than 0.001 by linear regression). These results support the use of probability analysis in the clinical diagnosis of coronary artery disease and provide a formal basis for comparing the relative diagnostic effectiveness and cost-effectiveness of different test combinations.

Adult↗
PubMedDate not supplied

Qualitative probability versus quantitative probability in clinical diagnosis: a study using a computer simulation.

The use of Bayes' theorem as a diagnostic tool in clinical medicine normally requires an input of exact probability estimates. However, humans tend to think in categories ("likely," "unlikely," etc.) rather than in terms of exact probability. A computer simulation of the presenting features of a case of pelvic infection has been used to compare the effects of quantitative and qualitative probability estimates on the diagnostic accuracy of Bayes' theorem. For the commoner conditions (prior probability greater than or equal to 0.2) the use of a two- or three-category system is virtually equivalent to the use of exact probability. However, uncommon conditions (prior probability less than or equal to 0.03) are completely ignored by the qualitative system. It is concluded that the use of simple categories of probability is acceptable for a Bayesian diagnostic system provided that the target conditions have a relatively high prior probability.

Artificial Intelligence↗
PubMed1996

The diagnostic information of tests for the detection of cancer: the usefulness of the likelihood ratio concept.

No single test is perfect and without false-negative and/or false-positive results. Consequently, the clinician is perpetually confronted with incertitude about the true disease state of the patient. In oncology, these diagnostic errors may have harmful consequences for the patient. It is, therefore, imperative that the clinician knows how often these errors occur, which implies a quantitative evaluation of a test. With this knowledge, the test result must subsequently be interpretated within the clinical framework. Bayes' theorem provides a simple and useful mathematical model for the integration of measures of test performance and clinical data. Traditionally, sensitivity and specificity are used to describe test performance. However, this approach requires that the conclusion of the test is dichotomised into 'normal' and 'abnormal'. Few tests have a natural binary outcome. A test parameter that is applicable to all types of test outcome scales and, at the same time, provides the opportunity to determine the gain in diagnostic information by applying Bayes' theorem, is therefore mandatory. The likelihood ratio meets these conditions. The application of this concept for both the evaluation and the interpretation of various types of tests used in cancer patients is demonstrated.

Bayes Theorem↗
PubMed1996

The diagnostic value of imaging the patient with dizziness. A Bayesian approach.

BACKGROUND: Imaging studies are routinely used in the evaluation of patients with dizziness. A principal concern of the ordering physician is to rule out a cerebellopontine angle (CPA) mass. The incidence of such masses in patients presenting with dizziness is quite low, however, raising the question of the value of imaging this population. OBJECTIVE: To calculate the probability, using Bayes theorem, that a given patient with dizziness has a CPA mass. DESIGN: Meta-analysis of epidemiological data on CPA masses and of studies reporting the incidence of otologic symptoms in patients with these masses. We also conducted a study of consecutive patients with dizziness to determine the frequency of asymmetric hearing loss in this population. These data were combined in applications of Bayes theorem to calculate disease probabilities. RESULTS: The probability that a patient with dizziness has a CPA mass is 0.0004, indicating that 2500 imaging studies would have to be performed to identify 1 CPA mass. If patients with subjectively normal hearing are investigated (ie, those with isolated dizziness), the probability is 0.000107, indicating that 9307 scans would have to be performed to identify 1 CPA mass. If the search is restricted to those patients with dizziness and asymmetric hearing loss (the patients usually felt to be high risk), the probability is 0.00156, indicating that 638 scans would have to be performed to identify 1 CPA mass. CONCLUSIONS: Even when studying patients with dizziness and asymmetric hearing loss, the probability of identifying a CPA mass is sufficiently low that we do not feel imaging is generally warranted. When faced with a patient with dizziness, we recommend a careful neurologic and otologic examination. If abnormalities are detected on examination that suggest central nervous system disease or invasive otologic disease, imaging should be pursued as appropriate. In cases of acute vertigo, if the patient is at high risk for cerebrovascular disease by virtue of age and additional risk factors, imaging should probably be pursued. For the remainder of patients, if progression of hearing loss is not documented, we do not believe imaging is warranted. Progressive hearing loss with abnormal speech reception thresholds probably warrants a magnetic resonance imaging scan of the internal auditory canals.

Aged↗
PubMed2005

Genetic risk estimation by healthcare professionals.

OBJECTIVES: To assess whether healthcare professionals correctly incorporate the relevance of a favourable test outcome in a close relative when determining the level of risk for individuals at risk for Huntington's disease. DESIGN AND SETTING: Survey of clinical geneticists and genetic counsellors from 12 centres of clinical genetics (United Kingdom, 6; The Netherlands, 4; Italy, 1; Australia, 1) in May-June 2002. Participants were asked to assess risk of specific individuals in 10 pedigrees, three of which required use of Bayes' theorem. PARTICIPANTS: 71 clinical geneticists and 41 other healthcare professionals involved in genetic counselling. MAIN OUTCOME MEASURES: Proportion of respondents correctly assessing risk in the three target pedigrees; proportion of respondents who were confident of their estimate. RESULTS: 50%-64% of respondents (for the three targets separately) did not include the favourable test information and incorrectly estimated the risks as being about equal to the prior risks; 77%-91% of these respondents were "sure" or "completely sure" that their estimations were correct. Twenty of the 112 respondents correctly estimated the risks for all three target pedigrees. CONCLUSIONS: Clinical geneticists and genetic counsellors frequently use prior risks in situations where Bayes' theorem should be applied, leading to overestimations of the risk for an individual.

Australia↗
PubMed2002

A nomogram to predict C282Y hemochromatosis.

Genetic testing of hemochromatosis has not been widely used as a diagnostic test because of unawareness of its existence and concerns about genetic discrimination. We developed a nomogram for the prediction of C282Y homozygotes for hemochromatosis from transferrin saturation and ferritin using Bayes theorem. The results of transferrin saturation and C282Y genotyping were available for 8,572 participants (5,042 men, and 3,530 women). The study group included patients in population-screening projects, referred cases, and family members. Likelihood ratios were calculated for transferrin saturation in predicting C282Y homozygotes. Pretest probabilities were estimated on the basis of serum ferritin concentration, and a predictive nomogram for men and women was created with the use of Bayes' theorem. In the highest-risk region of the nomogram in men, the probability of C282Y hemochromatosis was 89.7% (95% confidence interval = 85.1-94.3); in the lowest-risk zone it was 1.1% (0.4-1.9). The corresponding regions in women were 88.9% in the high zone (95% confidence interval = 77.0-100.0) and 6.5% in the lowest (95% confidence interval = 4.9-8.1). This approach allows the clinician to predict the probability of a patient's being a C282Y homozygote over a wide range of ferritin and transferrin saturation values instead of above a particular threshold.

Amino Acid Substitution↗
PubMedDate not supplied

Prediction of individual patient response to chemotherapy by the fluorometric microculture cytotoxicity assay (FMCA) using drug specific cut-off limits and a Bayesian model.

The semiautomated fluorometric microculture cytotoxicity assay (FMCA) based on the measurement of fluorescence generated from cellular hydrolysis of fluorescein diacetate (FDA) to fluorescein in microtiter plates, has been used for determination of cytotoxic drug resistance of tumor cells from patients with hematological and solid tumors. In the present study we describe a calibration procedure based on statistically derived cut-off limits and assay-predicted response probabilities using Bayes' theorem. Test results at a specified drug concentration were divided into three categories: low, intermediate or extreme drug resistance (LDR, IDR and EDR, respectively) using the median and median +1 standard deviation as the cut-off limits. When correlated with clinical outcome, LDR samples showed a higher response rate than expected, IDR a lower and EDR samples no response at all. The sensitivity and specificity of the test, using the median as cut-off limit, were 0.92 and 0.69 respectively. By fitting these test characteristics to a statistical model based on Bayes' theorem it is possible to calculate response probabilities for each individual patient taking into consideration not only the test characteristics and the particular assay result, but also the clinical and patient specific characteristics influencing the pre-test probability of response. EDR predicts clinical drug resistance with high specificity and is also observed in tumor types with high response rate.

Antineoplastic Agents↗
PubMed1985

Probability of vaginal foreign body in girls with genital complaints.

We used Bayes' theorem to evaluate the probability of vaginal foreign bodies in girls with genital complaints. The prevalence of vaginal foreign bodies in outpatient girls under 13 years of age with gynecologic disorders was found to be 4%. Review of the charts of 17 pre-menarcheal girls who had foreign bodies on 19 occasions and of 28 girls with infectious or nonspecific vaginitis demonstrated that patients with foreign bodies reported vaginal bleeding more frequently than discharge (14 and two of 15 symptomatic cases, respectively), while in contrast, patients with infectious or nonspecific vaginitis reported vaginal discharge more often than bleeding (23 and five of 28 symptomatic controls, respectively). Bayes' theorem indicates that, in populations resembling the one we studied, approximately 18% of preteenage girls with vaginal bleeding with or without discharge and 50% of those with bleeding and no discharge will prove to have vaginal foreign bodies.

Adolescent↗
PubMed1998

Decision-analysis methodology in the work-up of women with suspected silicone breast implant rupture.

Despite numerous studies advocating ultrasonography and magnetic resonance imaging (MRI) in the evaluation of women with possible silicone breast implant rupture, an appropriate algorithm has not been published for the optimal use of these tests. To derive a diagnostic algorithm using ultrasonography and MRI, we applied a decision-analytic model using Bayes' theorem to calculate the probabilities of implant rupture for three representative patient characteristics. A Medline search was conducted to identify literature related to the diagnosis of silicone breast implant rupture using ultrasonography and MRI since 1994. Also examined were case series of implant rupture to obtain rupture prevalence in cases in which rupture was dependent on patient presentation (symptomatic versus asymptomatic) and dependent on implant age. Test characteristics (sensitivity and specificity) and implant rupture prevalence are used to calculate the probability of rupture by using Bayes' theorem. These probabilities are derived for three patient categories: (1) asymptomatic, (2) symptomatic with implant age < or = 10 years, and (3) symptomatic with implant age >10 years. In asymptomatic patients, the pretest rupture prevalence is 6.5 percent. If a screening ultrasonography shows no rupture, the probability of rupture drops to 2.2 percent. If ultrasonography shows rupture, the probability of true rupture increases to 37.8 percent. Removal of implants in this setting will result in a high probability of extracting normal implants. However, if MRI after the ultrasonography shows rupture, the probability of true rupture increases to 86 percent, which gives better assurance of removing true-ruptured implants. In "symptomatic" patients (i.e., breast asymmetry, capsular contracture) with implants < or = 10 years old, the prevalence of rupture is estimated at 31 percent. If ultrasonography shows no rupture, the probability of rupture drops to 16 percent. If ultrasonography shows rupture, the probability of true rupture is 79.7 percent, and this probability increases to 97.5 percent if a follow-up MRI also shows rupture. In symptomatic patients with implants >10 years old, the prevalence of rupture is estimated at 64 percent. If ultrasonography shows rupture, the probability of true rupture increases to 94 percent, and no further diagnostic work-up is necessary. In an asymptomatic patient who is worried about the integrity of her implants, ultrasonography should be used as an initial diagnostic test because of its lower cost. If ultrasonography shows no rupture, no further work-up is necessary. If ultrasonography shows rupture, the low probability (37.8 percent) of true rupture requires a confirmatory test using MRI. In "symptomatic" patients, the high prevalence of rupture markedly raises the posttest probability of rupture for positive ultrasonography findings. Particularly in "symptomatic" patients with implants >10 years old, the high posttest probability of rupture (94 percent) with a positive ultrasonography obviates the need for any further diagnostic testing. This diagnostic algorithm will assist plastic surgeons in counseling women who are worried about the integrity of their silicone breast implants.

Bayes Theorem↗
PubMed1994

The effect of changing disease risk on clinical reasoning.

OBJECTIVE: To assess the ability of health care professionals to evaluate the effect of clinical test results in different settings. DESIGN: Subjects were presented with a series of generic clinical scenarios in which information about the test performance and the pretest probability of disease was varied. The subject estimates of posttest probability were compared with those calculated on the basis of Bayes' theorem. PARTICIPANTS: Fifty health care professionals, including 31 physicians and 19 nonphysicians, associated with a university teaching hospital. MEASUREMENTS AND MAIN RESULTS: Under a variety of testing conditions, both the physicians and the nonphysicians inaccurately estimated the posttest probability of disease. Based on a logarithmic transformation, the error in probability estimation was divided into a portion related to the pretest probability of disease and a portion related to the test performance. Most of the error in posttest probability estimation was associated with the incorrect use of pretest probabilities. The subjects consistently overestimated the posttest probability of disease expected under Bayes' theorem, with increasing error associated with decreasing pretest probability. Physician estimates of posttest probability increased with increasing likelihood ratios for each scenario. Nonphysician estimates of posttest probabilities increased with increasing likelihood ratios for a positive test, but the estimates associated with a negative test result were inconsistent. CONCLUSIONS: Physicians and nonphysicians overestimate posttest probabilities with increasing error associated with decreasing disease risk. Some nonphysicians may not fully understand the effect of test performance on risk estimation, particularly in the setting of a negative test. Health care professionals should receive training in the proper evaluation of test information, with particular emphasis on the influence of pretest disease risk on the posttest probability of disease.

Bayes Theorem↗
PubMed2002

Quantitative evaluation of the diagnostic thinking process in medical students.

OBJECTIVE: To explore the diagnostic thinking process of medical students. SUBJECTS AND METHODS: Two hundred twenty-four medical students were presented with 3 clinical scenarios corresponding to high, low, and intermediate pre-test probability of coronary artery disease. Estimates of test characteristics of the exercise stress test, and pre-test and post-test probability for each scenario were elicited from the students (intuitive estimates) and from the literature (reference estimates). Post-test probabilities were calculated using Bayes' theorem based upon the intuitive estimates (Bayesian estimates of post-test probability) and upon the reference estimates (reference estimates of post-test probability). The differences between the reference estimates and the intuitive estimates, and between Bayesian estimates and the intuitive estimates were used for assessing knowledge of test characteristics, and ability of estimating pre-test and post-test probability of disease. RESULTS: Medical students could not rule out disease in low or intermediate pre-test probability settings, mainly because of poor pre-test estimates of disease probability. They were also easily confused by test results that differed from their anticipated results, probably because of their inaptitude in applying Bayes' theorem to real clinical situations. These diagnostic thinking patterns account for medical students or novice physicians repeating unnecessary examinations. CONCLUSIONS: Medical students' diagnostic ability may be enhanced by the following educational strategies: 1) emphasizing the importance of ruling out disease in clinical practice, 2) training in the estimation of pre-test disease probability based upon history and physical examination, and 3) incorporation of the Bayesian probabilistic thinking and its application to real clinical situations.

Adult↗
PubMed2003

A Bayesian argument against rigid cut-offs in electrodiagnosis of median neuropathy at the wrist.

BACKGROUND: Nerve conduction (NC) tests, using rigid cut-offs separating normal from abnormal test values, are commonly used to confirm median neuropathy at the wrist (MNW). The authors studied patients with clinically defined mild MNW and a normal median distal motor latency to determine 1) how much sensory or mixed NC test results increase (or decrease) the probability of MNW and 2) the NC test values required to confirm (or exclude) MNW for the range of pretest probabilities of MNW. METHODS: Palmar, digit 4 (D4), and digit 2 (D2) median NC tests were reviewed in 125 hands with mild carpal tunnel syndrome (CTS) and 100 control hands with musculoskeletal pain. Receiver operating characteristic curves and interval likelihood ratios were plotted for the three tests. Using Bayes theorem, post-test probability of MNW was then determined for the range of pretest probabilities and NC test values. RESULTS: Receiver operating characteristic curves showed that for a set specificity of 97%, palmar and D4 studies had higher electrodiagnostic utility than D2 studies with cut-off test values (sensitivities of 0.3 msec, 64.0%; 0.4 msec, 71.2%; and 50 m/sec, 44.8%). However, Bayesian analysis showed that to confirm MNW more conservative cut-off values (palmar 0.5 msec, D4 0.7 msec, D2 44 m/sec) were required for pretest probabilities or=75%. Conversely, normal test values could exclude MNW only for pretest probabilities <25%. CONCLUSIONS: For a given NC test value, post-test probability of MNW can be determined from the estimated pretest probability (derived from clinical data), interval likelihood ratios, and Bayes theorem. Use of rigid cut-off values to confirm MNW is problematic, because more conservative cut-offs are required for low pretest probability. Conversely, NC tests with sensitivity <95% cannot exclude MNW when pretest probability is high.

Adolescent↗
PubMed1979

Emergency room resuscitation of patients with cardiac arrest outside hospital. Outcome and immediate prognosis in 319 patients.

Resuscitation was attempted in 319 patients brought to hospital with cardiac arrest during a 5-year period. Primary successful results were achieved in 50 patients (15.7%). Twelve patients were long-term survivors (3.4%), 10 of whom had normal brain function, whereas 2 had mild cerebral dysfunction. To improve prognostication in patients with initially successful resuscitation, Bayes' theorem was applied using 4 clinical findings after 24 hours' treatment: reactions to painful stimuli, pupillary size, light reactions and BP, Bayes' theorem as well as coma depth after 24 hours gave valuable information regarding individual prognosis.

Aged↗