A peripartum neurologic event: shooting from the hip

Cohort decomposition for Markov cost-effectiveness models

Cohort analysis is a widespread tool for computing expected costs and quality-adjusted life years (QALYs) in Markov models for medical cost-effectiveness analyses. Although not always explicitly identified, such models commonly have multiple simple factors, or components. In these, a health state consists of a multiple component vector, one component for each factor, and arbitrary combinations of components are possible. The authors show here that when the model does not assume any probabilistic dependence among these factors, then a standard cohort analysis may be decomposed into several independent cohort analyses, one for each factor, and the results may be combined to produce desired expected costs and QALYs. These single-factor cohort analyses are not only simpler but also computationally more efficient. The authors derive the appropriate formulas for this cohort decomposition in discrete time and give several examples of their use based on published cost-effectiveness analyses. Explicitly identifying the simple factors of which a model is composed allows these factors to be portrayed graphically. Graphical depiction of the simple factors that comprise a model reduces model complexity, makes model formulation easier and more transparent, and thereby facilitates peer inspection and critique.

A linear index for predicting joint health-states utilities from single health-states utilities

Direct elicitation of utilities for joint health (JS) states may pose substantial interview burden, while traditional models to predict these utilities from utilities of component single states (SS) are inconsistent with the data. Using individual-level data on utilities for health states associated with prostate cancer, we report the performance of a new model that encompasses three traditional models - additive, multiplicative, and minimum - previously used for predicting utilities for joint health states. Describing utilities in terms of utility losses l(.) relative to prefect health, our final estimated linear index for predicting joint health-state utilities is El(JS)=0.05+0.72 x max l(SS1),l(SS2)+0.33.min x l(SS1),l(SS2)-0.18 x l(SS1) x l(SS2). Based on out-of-sample predictions, this model produces up to 50% reduction in mean-square error compared with traditional models and consistent prediction across different ranges of joint-state utilities, which the traditional models do not. Parameter estimates of the new model proposed here provide direct evidence on the inconsistencies of the traditional models, are grounded in psychological theory by emphasizing the more severe component of a joint health state, and provide a simple linear index to generate consistent predictions of utilities for joint health states. Further validation of this function for joint health states in other clinical scenarios is warranted.

In search of tools to aid logical thinking and communicating about medical decision making

To have real-time impact on medical decision making, decision analysts need a wide variety of tools to aid logical thinking and communication. Decision models provide a formal framework to integrate evidence and values, but they are commonly perceived as complex and difficult to understand by those unfamiliar with the methods, especially in the context of clinical decision making. The theory of constraints, introduced by Eliyahu Goldratt in the business world, provides a set of tools for logical thinking and communication that could potentially be useful in medical decision making. The author used the concept of a conflict resolution diagram to analyze the decision to perform carotid endarterectomy prior to coronary artery bypass grafting in a patient with both symptomatic coronary and asymptomatic carotid artery disease. The method enabled clinicians to visualize and analyze the issues, identify and discuss the underlying assumptions, search for the best available evidence, and use the evidence to make a well-founded decision. The method also facilitated communication among those involved in the care of the patient. Techniques from fields other than decision analysis can potentially expand the repertoire of tools available to support medical decision making and to facilitate communication in decision consults.

The physician-patient encounter: the physician as a perfect agent for the patient versus the informed treatment decision-making model

Assuming a goal of arriving at a treatment decision which is based on the physician's knowledge and the patient's preferences, we discuss the feasibility of implementing two treatment decision-making models: (1) the physician as a perfect agent for the patient, and (2) the informed treatment decision-making models. Both models fall under the rubric of agency models, however, the requirements from the physician and the patient are different. An important distinction between the two models is that in the former the patient delegates authority to her doctor to make medical decisions and thus the challenge is to encourage the physician to find out the patient's preferences. In the latter, the patient retains the authority to make medical decisions and the physician role is that of information transfer. The challenge here is to encourage the physician to transfer the knowledge in a clear and nonbiased way. We argue that the choice of model depends among other things on the ease of implementation (e.g., is it simpler to transfer patient's preferences to doctors or to transfer technical knowledge to patients?). Also the choice of treatment decision-making model is likely to have an impact on the type of incentives or regulations (i.e., contracts) needed to promote the chosen model. We show that in theory both models result in the same outcome. We argue that the approach of transferring information to the patient is easier (but not easy) and, hence, more feasible than transferring each patient's preferences to the physician in each medical encounter. We also argue that because better "technology" exists to transfer medical information to patients and time costs are involved in both tasks (i.e. transferring preferences or information), it is more feasible to design contracts to motivate physicians to transfer information to patients than to design contracts to motivate physicians to find out their patients' utility functions. We illustrate our arguments using a clinical example of the choice of adjuvant chemotherapy versus no adjuvant chemotherapy for women with early stage breast cancer. We also discuss issues relating to the current realities of clinical practice and their potential implications for the way that economists model physician-patient clinical encounters.

Decision analysis in the clinical neurosciences: a systematic review of the literature

Clinical decision analysis can be a useful scientific tool for individual patient management, for planning of clinical research and for reaching consensus about clinical problems. We systematically reviewed the decision analytic studies in the clinical neurosciences that were published between 1975 and July 1994. All studies were assessed on aspects of clinical applicability: presence of case and context description, completeness of the analysed strategies from a clinical point of view, extendibility of the analyses to different patient profiles, and up-to-date-ness. Fifty-nine decision analyses of twenty-eight different clinical problems were identified. Twenty-eight analyses were based on the theory of subjective expected utility, twelve on cost-effectiveness analysis. Four studies used ROC analysis, and fifteen were risk-, or risk-benefit analyses. At least six studies could have been improved by more elaborately disclosing the context of the clinical problem that was addressed. In eleven studies, the effect of different, yet plausible assumptions was not explored, and in eighteen studies the reader was not informed how to extend the results of the analysis to patients with (slightly) different clinical characterisitics. All studies had, by nature, the potential to promote insight into the clinical problem and focus the discussion on clinically important aspects, and gave clinically useful advice. We conclude that clinical decision analysis, as an explicit, quantitative approach to uncertainty in decision making in the clinical neurosciences will fulfill a growing need in the near future.

Surgery of unruptured, asymptomatic aneurysms: a decision analysis

BACKGROUND

Asymptomatic cerebral aneurysms are diagnosed more frequently since the advent of computed tomography and magnetic resonance imaging. Their management is currently empirical. We have used decision analysis to place it on a more analytical basis.

METHODS

Decision analysis was used to determine the benefit in years of survival free of sequelae resulting from elective surgery of unruptured aneurysms over natural history. We took 2% as the annual rate of rupture (r), 73% as the risk of death or disability with rupture (M), and 6.5% for the average risk of elective surgery (S). Benefit was calculated from the equation L([1-(1-r)L]M/2-S) [1] for life expectancy (L) corresponding to each quinquennial age group from age 15 to 100 years. Sensitivity analysis was performed to take into account increasing risk of elective surgery based on the size, and accessibility of the aneurysm, and variable risks of rupture and outcome.

RESULTS

A gain of at least one year of survival free of neurological sequelae is achieved by surgery compared to natural history for patients whose life expectancy is 19.5 years, corresponding to age 63.5 years for males and 68 years for females. The life expectancy at which a benefit accrues is longer (the patient is younger) for larger, less accessible aneurysms, for lower rates of rupture, and for lesser risks of death or disability from rupture.

CONCLUSIONS

Elective surgery of unruptured asymptomatic aneurysms achieves an increased survival over the natural history of at least one year free of neurological sequelae in patients whose life expectancy is 19.5 years or more, using our baseline assumptions. Using equation [1], the corresponding life expectancy producing this benefit can be calculated to account for the increased surgical risk of large, poorly accessible aneurysms and for factors affecting natural history.

Angiographic screening and elective surgery of familial cerebral aneurysms: a decision analysis

Up to 6% of cerebral aneurysms may be familial. Because the pattern of inheritance and the prevalence of aneurysms within families are unknown, the management of family members at risk of harboring a cerebral aneurysm is currently empirical. We established the prevalence of aneurysms in the second generation of individuals with familial cerebral aneurysms and determined the possible benefit of angiographic screening and elective surgery of such individuals by using a simple decision analysis model. Four consecutive families were identified in whom the mother and a child had a ruptured cerebral aneurysm. A total of 19 siblings at risk in the second generation were identified. Fifteen underwent elective cerebral angiography: one had a cerebral aneurysm and two had an infundibulum at the origin of the posterior communicating artery. Including the previously known aneurysms, the prevalence of aneurysms in the second generation was thus established at 29.4%. A decision analysis was performed with 2% as the annual risk of rupture, 72.7% as the risk of death or disability with rupture, 0.1% as the risk of angiography, and 6.5% as the risk of surgery. The benefit in years of survival free of sequelae resulting from angiographic screening and elective surgery (intervention) over natural history was computed for life expectancy corresponding to each quinquennial age group from age 15 to 100 years. Intervention equaled natural history, in terms of years of survival expected with each choice, at a life expectancy of 10.6 years, corresponding to age 76.5 years for men and 80 years for women, and produced a net gain of at least 1 year for patients whose life expectancy was 32 years or more, corresponding to age 53.5 years for women and 49 years for men. Greater benefit was achieved with increasing life expectancy (younger age). The prevalence of aneurysms in the second generation when a mother and child have an aneurysm is 29.4%. Intervention produces a benefit of at least 1 year of survival free of sequelae over natural history in such individuals if their life expectancy is 32 years or more.

Decision analysis--a helpful tool for clinicians to establish diagnostic-therapeutic guidelines?

In this paper we focus on the question: Does decision analysis provide a framework to assess the value of diagnostic tests in clinical practice and how can it be used by clinicians in establishing diagnostic-therapeutic guidelines. To study this question we performed two analyses concerning the use of pelvic lymphadenectomy and pedal lymphography for staging prostate cancer. Both analyses yielded similar results as far as the preferred strategy was concerned, yet the approach and set up of the two analyses were different. The first analysis was performed in accordance with the textbooks on decision analysis. However, using this traditional approach we encountered some difficulties: in structuring the decision tree, in eliciting values for the quality of life parameters, and in interpreting the results. These difficulties urged us to modify the approach, presented in the second analysis. In this second analysis, the decision problem was split into several consecutive decision problems which corresponded to the questions posed by the clinicians. Longevity and quality of life were considered separately and the consequences of treatment and testing, which affect the quality of life of the patients, were indicated by just two parameters. Finally, the result of the analysis was expressed in clinically meaningful terms. The second analysis is compared with different approaches presented in the literature for analyzing decision problems involving diagnostic tests. Despite some unresolved methodological problems it is concluded that decision analysis provides a good framework for clinicians to structure and analyze complex decision problems.

Stochastic trees: a new technique for temporal medical decision modeling

This paper introduces stochastic trees, a new modeling approach for the class of medical decision problems in which risks of mortality and morbidity may extend over time. A stochastic tree may be regarded as a continuous-time version of a Markov-cycle tree, or alternately, as a multi-state DEALE model. Optimal decisions in stochastic trees can be determined by rollback, much in the same fashion as decision trees. The author discusses how age-dependent mortality rates and declining incidence rates may be modeled using stochastic trees. Concepts are illustrated using examples from the medical literature. It is argued that stochastic trees possess important advantages over Markov-cycle trees for medical decision modeling.

Familial intracranial aneurysms. A review

BACKGROUND

A familial occurrence of intracranial aneurysms is defined by the presence of such aneurysms in two or more first to third-degree family members. Families with two affected members may represent accidental aggregation. Other families show a frequency compatible with an autosomal dominant mode of inheritance. A genetic basis is also suggested by the younger average age of familial cases with a ruptured intracranial aneurysm (42.3 years versus an age range of 50-54 years for nonfamilial cases), occurrence at the same site or a mirror site in sibling pairs, occurrence in identical twins, and the association of intracranial aneurysms with genetically transmitted disorders.

SUMMARY OF REVIEW

No reliable data are available about the occurrence of familial intracranial aneurysms among all patients with ruptured aneurysms; a frequency of 6.7% has been reported from a retrospective study, but a large part of the "familial" occurrence can be explained by fortuitous aggregation. The pathogenesis of familial intracranial aneurysms is not fully explained; a (partial) deficiency of type III collagen has been reported in sporadic, but not in familial, cases. Clinical decision analysis shows how the risk of harboring an intracranial aneurysm and the age of the patient are the main determinants for elective screening; lifetime risk of rupture (and therefore age) and surgical risks are the determinants for neurosurgical treatment.

CONCLUSIONS

Surgical treatment is recommended for patients aged less than 70 years with a moderate or low surgical risk, and screening (preferably by intra-arterial digital subtraction angiography) is recommended only for relatives aged 35-65 years. Magnetic resonance angiography may develop into a useful alternative for screening, but the risks of diagnostic procedures play only a minor role in the decision analysis.

Aortic aneurysm in a 74-year-old man with coronary disease and obstructive lung disease: is double jeopardy enough?

A previous decision analysis examined a patient with severe CAD, diminished ventricular function, and an abdominal aortic aneurysm and also concluded that CABG followed by aneurysm repair was optimal. This patient, who had well-preserved cardiac function but severely compromised pulmonary status, stood to gain less from CABG than would a patient with more severe coronary disease, thus accounting for the "close-call" between the CABG-AAA and AAA only strategies. Nevertheless, the analysis did emphasize the benefit of aneurysm repair, whether done alone or after CABG. The analysis also highlighted the significant risk of aneurysm rupture the patient is exposed to while recovering from CABG surgery. The operative mortality risks of the two procedures are similar; thus, the patient's total operative risk is approximately doubled if he undergoes both procedures rather than aneurysm repair alone. The key question raised by the analysis is whether this double jeopardy is more than compensated by the degree to which prior CABG reduces both short-term cardiac risk at subsequent aneurysm repair and long-term cardiac mortality. For this patient, who had good cardiac function, the gains appeared sufficient to offset the interval risk of aneurysm rupture and the additional risk associated with a surgical procedures. THE REAL WORLD The patient indeed underwent and tolerated CABG, although he had a stormy prolonged postoperative course due to pulmonary failure. After discharge from the hospital, he declined readmission for repair of the aneurysm. We did not model that possibility, clearly an inadequacy in our tree. Some six months later, the patient was still alive and was, reluctantly, readmitted for aneurysmorrhaphy. At that time, however, his pulmonary function had deteriorated and both the anesthesiologist and the pulmonary consultant stated unequivocally that further surgery was now impossible. In retrospect, the expected utility of CABG without aneurysm repair (thus providing only a decrease in the long-term mortality risk from his CAD) would have been 1.95 (DEALE) or 2.06 (Markov) years. Sensitivity analysis revealed that, even if long-term cardiac risk were completely eliminated by CABG, immediate aneurysm repair would have been a better approach had the patient's physicians known he would be likely to refuse or not be a candidate for the second operation. In summary, although the patient's comorbidities did indeed place him at significant operative risk for either aneurysmorrhaphy alone or two sequential procedures, the benefits to be gained were shown to far outweigh the risks when compared with expectant observation.(ABSTRACT TRUNCATED AT 400 WORDS)