Equation and Nomogram for Calculation of Testing and Treatment Thresholds

Djulbegoviĉ, Benjamin; Desoky, A.

doi:10.1177/0272989x9601600215

Cited by 15 publications

(14 citation statements)

References 3 publications

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“…The method is suitable for determining the benefit of incorporating alternative diagnostic methods relative to current practice, independent of test performance criteria such as ROC-AUC, sensitivity and specificity, and study sample size. An advantage of DCA is that it does not require knowledge of all the possible outcomes of clinical decisions typically required for classical decision-analytic methodology [25]. …”

Section: Resultsmentioning

confidence: 99%

Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

et al. 2013

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BackgroundThe prognosis of pancreatic cancer (PC) is one of the poorest among all cancers, due largely to the lack of methods for screening and early detection. New biomarkers for identifying high-risk or early-stage subjects could significantly impact PC mortality. The goal of this study was to find metabolic biomarkers associated with PC by using a comprehensive metabolomics technology to compare serum profiles of PC patients to healthy control subjects.MethodsA non-targeted metabolomics approach based on high-resolution, flow-injection Fourier transform ion cyclotron resonance mass spectrometry (FI-FTICR-MS) was used to generate comprehensive metabolomic profiles containing 2478 accurate mass measurements from the serum of Japanese PC patients (n=40) and disease-free subjects (n=50). Targeted flow-injection tandem mass spectrometry (FI-MS/MS) assays for specific metabolic systems were developed and used to validate the FI-FTICR-MS results. A FI-MS/MS assay for the most discriminating metabolite discovered by FI-FTICR-MS (PC-594) was further validated in two USA Caucasian populations; one comprised 14 PCs, six intraductal papillary mucinous neoplasims (IPMN) and 40 controls, and a second comprised 1000 reference subjects aged 30 to 80, which was used to create a distribution of PC-594 levels among the general population.ResultsFI-FTICR-MS metabolomic analysis showed significant reductions in the serum levels of metabolites belonging to five systems in PC patients compared to controls (all p<0.000025). The metabolic systems included 36-carbon ultra long-chain fatty acids, multiple choline-related systems including phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, as well as vinyl ether-containing plasmalogen ethanolamines. ROC-AUCs based on FI-MS/MS of selected markers from each system ranged between 0.93 ±0.03 and 0.97 ±0.02. No significant correlations between any of the systems and disease-stage, gender, or treatment were observed. Biomarker PC-594 (an ultra long-chain fatty acid), was further validated using an independently-collected US Caucasian population (blinded analysis, n=60, p=9.9E-14, AUC=0.97 ±0.02). PC-594 levels across 1000 reference subjects showed an inverse correlation with age, resulting in a drop in the AUC from 0.99 ±0.01 to 0.90 ±0.02 for subjects aged 30 to 80, respectively. A PC-594 test positivity rate of 5.0% in low-risk reference subjects resulted in a PC sensitivity of 87% and a significant improvement in net clinical benefit based on decision curve analysis.ConclusionsThe serum metabolome of PC patients is significantly altered. The utility of serum metabolite biomarkers, particularly PC-594, for identifying subjects with elevated risk of PC should be further investigated.

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Section: Resultsmentioning

confidence: 99%

Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

et al. 2013

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“…51, 55À57 The solution, based on EUT, provides the following rational axiom for physicians' behavior: If the probability of the disease is larger than p rx , the treatment should be administered without testing (the disease is considered ''ruled in''); if the probability of disease is smaller than p tt , the patient should continue to be observed (the disease is considered ''ruled out''); and if the probability of the disease is between the 2 thresholds, the test should be administered and the patient treated or observed based on the test result. 51,55,57 However, after making a decision under uncertainty, one may discover that another alternative would have been preferable. This knowledge may bring a sense of loss or regret.…”

Section: Methodsmentioning

confidence: 99%

“…With the classical utilities, described as shown in Figure 1, the thresholds are calculated as follows 51,53,55,57 :…”

Section: Comparison With Classical Expected Utilitiesmentioning

confidence: 99%

When Is Diagnostic Testing Inappropriate or Irrational? Acceptable Regret Approach

Hozo

Djulbegoviĉ

2008

Med Decis Making

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The authors provide a new model within the framework of theories of bounded rationality for the observed physicians' behavior that their ordering of diagnostic tests may not be rational. Contrary to the prevailing thinking, the authors find that physicians do not act irrationally or inappropriately when they order diagnostic tests in usual clinical practice. When acceptable regret (i.e., regret that a decision maker finds tolerable upon making a wrong decision) is taken into account, the authors show that physicians tend to order diagnostic tests at a higher level of pretest probability of disease than predicted by expected utility theory. They also show why physicians tend to overtest when regret about erroneous decisions is extremely small. Finally, they explain variations in the practice of medicine. They demonstrate that in the same clinical situation, different decision makers might have different acceptable regret thresholds for withholding treatment, for ordering a diagnostic test, or for administering treatment. This in turn means that for some decision makers, the most rational strategy is to do nothing, whereas for others, it may be to order a diagnostic test, and still for others, choosing treatment may be the most rational course of action.

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“…One such attractive model-Dual System Model (DSM)- has been developed by Mukherjee [11]. Here, we extend Mukherjee’s DSM model to medical field (DSM-M) by linking it to the threshold concept of decision-making [12-15]. We also take into account decision regret, as an exemplar of affect or emotion that is involved in system I decision-making [2], and which is of particular relevance to medical decision-making [16-19].…”

Section: Introductionmentioning

confidence: 99%

Dual processing model of medical decision-making

Djulbegoviĉ

Hozo

Beckstead

et al. 2012

BMC Med Inform Decis Mak

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114

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BackgroundDual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease.MethodsWe developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice.ResultsWe show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice.ConclusionsWe have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).

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Equation and Nomogram for Calculation of Testing and Treatment Thresholds

Cited by 15 publications

References 3 publications

Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

Metabolic system alterations in pancreatic cancer patient serum: potential for early detection

When Is Diagnostic Testing Inappropriate or Irrational? Acceptable Regret Approach

Dual processing model of medical decision-making

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