Kate Bull

Presentation and attrition in complex pulmonary atresia

OBJECTIVES This study summarized patterns of presentation and attrition in complex pulmonary atresia. BACKGROUND Assessment of the potential impact of surgical strategies for managing complex pulmonary atresia requires information about variability in age and physiology at presentation of the condition. METHODS We performed a retrospective review of age at presentation, referral source, pulmonary artery and collateral anatomy and surgical history of 218 patients from two institutions dealing with congenital heart disease throughout life. RESULTS Approximately 65% of pulmonary atresia appears in infancy, with 50% of patients severely symptomatic from cyanosis and 25% from heart failure. Compared with those presenting undiagnosed, patients referred secondarily for specialist management tend to be older when first seen, and care must be taken when generalizing about the natural history of the condition from their survival experience. Overall actuarial survival, including the effects of operation, suggests that 60% (95% confidence limits [CL] 43 to 73) of patients presenting in infancy survive to their first birthday, 65% (95% CL 51 to 74) of those alive at 1 year old survive to the age of 10, and 16% (95% CL 5 to 31) of those alive at 10 years old survive to age 35. CONCLUSIONS Novel surgical approaches have generally been applied beyond infancy in patients selected by their survival through the period of greatest attrition for this disease. Unless successful application in symptomatic infants is demonstrated, we cannot assume that these serial and complicated operations will have a major impact on the outlook of most patients with complex pulmonary atresia.

Tutorial in biostatistics survival analysis in observational studies

SUMMARY Multi-centre databases are making an increasing contribution to medical understanding. While the statistical handling of randomized experimental studies is well documented in the medical literature, the analysis of observational studies requires the addressing of additional important issues relating to the timing of entry to the study and the e⁄ect of potential explanatory variables not introduced until after that time. A series of analyses is illustrated on a small data set. The influence of single and multiple explanatory variables on the outcome after a fixed time interval and on survival time until a specific event are examined. The analysis of the e⁄ect on survival of factors that only come into play during follow-up is then considered. The aim of each analysis, the choice of data used, the essentials of the methodology, the interpretation of the results and the limitations and underlying assumptions are discussed. It is emphasized that, in contrast to randomized studies, the basis for selection and timing of interventions in observational studies is not precisely specified so that attribution of a survival e⁄ect to an intervention must be tentative. A glossary of terms is provided. ( 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 1041—1074 (1997).

Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System

Abstract Three paediatric cardiologists assessed nearly 1000 imprecise subjective conditional probabilities for a simple belief network representing congenital heart disease, and the quality of the assessments has been measured using prospective data on 200 babies. Quality has been assessed by a Brier scoring rule, which decomposes into terms measuring lack of discrimination and reliability. The results are displayed for each of 27 diseases and 24 questions, and generally the assessments are reliable although there was a tendency for the probabilities to be too extreme. The imprecision allows the judgements to be converted to implicit samples, and by combining with the observed data the probabilities naturally adapt with experience. This appears to be a practical procedure even for reasonably large expert systems.

Physiological and Phenotypic Characteristics of Late Survivors of Tetralogy of Fallot Repair Who Are Free From Pulmonary Valve Replacement

Background— Pulmonary valve replacement (PVR) after repair of tetralogy of Fallot is commonly required and is burdensome. Detailed anatomic and physiologic characteristics of survivors free from late PVR and with good exercise capacity are not well described in a literature focusing on the indications for PVR. Methods and Results— Survival and freedom from PVR were tracked in 1085 consecutive patients receiving standard tetralogy of Fallot repair in a single institution from 1964 to 2009. Of 152 total deaths, 100 occurred within the first postoperative year. Surviving patients between 10 and 50 years of age had an annual risk of death of 4 (confidence limit, 2.8–5.4) times that of normal contemporaries. To date, 189 patients have undergone secondary PVR at mean age of 20±13 years (36% of those alive at 40 years of age). A random sample of 50 survivors (age, 4–57 years) free from PVR underwent cardiovascular magnetic resonance, echocardiography, and exercise testing. These patients had mildly dilated right ventricles (right ventricular end-diastolic volume=101±26 mL/m2) with good systolic function (right ventricular ejection fraction=59±7%). Most had exercise capacity within normal range (z peak O2=−0.91±1.3; z E/ CO2=0.20±1.5). In patients >35 years of age with normal exercise capacity, there was mild residual right ventricular outflow tract obstruction (mean gradient, 24±13 mm Hg), pulmonary annulus diameters <0.5z, and unobstructed branch pulmonary arteries. Conclusions— An important proportion of patients require PVR late after tetralogy of Fallot repair. Patients surviving to 35 years of age without PVR and with a normal exercise capacity may have had a definitive primary repair; their right ventricular outflow tracts are characterized by mild residual obstruction and pulmonary annulus diameter <0.5z.

Empirical Evaluation of Prior Beliefs about Frequencies: Methodology and a Case Study in Congenital Heart Disease

Abstract We consider the problem of critiquing prior beliefs concerning the distribution of a discrete random variable in the light of a sequentially obtained sample. A topical application concerns a probabilistic expert system for the diagnosis of congenital heart disease, which requires specification of a large number of conditional probabilities that are initially imprecisely estimated by a suitable “expert.” These prior beliefs may be formally updated as data become available, but it would seem essential to contrast the original expert assessments with the data obtained to quickly identify inappropriate subjective inputs. We consider both Bayes factor and significance testing techniques for such a prior/data comparison, both in nonsequential and sequential forms. The common basis as alternative standardizations of the logarithm of the predictive ordinate of the observed data is emphasised, and a Bayesian discrepancy statistic with a variety of interpretations provides a formal means of discounting the...

Evaluation of a diagnostic algorithm for heart disease in neonates.

OBJECTIVE--To develop, test, and validate an algorithm for diagnosing disease in neonates during an over the telephone referral to a specialist cardiac centre. DESIGN--A draft algorithm requiring only data available to a referring paediatrician was generated. This was modified in the light of a retrospective review of case records. A questionnaire to elicit all the data required by the algorithm was then generated. There followed a prospective three phase evaluation during consecutive over the telephone referrals. This consisted of (a) a conventional phase with unstructured referral consultations, (b) a phase with referrals structured around the questionnaire but independent of the algorithm, and (c) a validation phase with the algorithm (and its previous errors) available during the referral consultation. SETTING--59 paediatric centres in south east England and a central specialist paediatric cardiology unit. PATIENTS--Consecutive neonates (aged less than 31 days) referred with suspected heart disease. The retrospective review was of records of 174 neonates from 1979. In the prospective evaluation (1987-90) the conventional phase comprised 71 neonates (over 5.5 months), the structured phase 203 neonates (over 14 months), and the validation phase 195 neonates (over 12 months). MAIN OUTCOME MEASURES--Diagnostic accuracy (assigning patients to the correct diagnostic category (out of 27)), of the referring paediatrician, the specialist after the referral consultation, and the algorithm as compared with the definitive diagnosis by echocardiography at the specialist centre, and score for the appropriateness of management in transit. RESULTS--Simply structuring the consultation by questionnaire (that is, proceeding from the conventional phase to the structured phase) improved the diagnostic accuracy of both paediatricians (from 34% (24/71 cases) to 48% (97/203) correct) and specialists (from 54% (38/71 cases) to 64% (130/203) correct). The algorithm (structured phase) would have been even more accurate (78% (158/203 cases); p less than 0.01). Management scores in the structured phase were also better than in the conventional phase (80%(162/203 cases)v 58% (41/71) appropriate; p less than 0.01). Management scores would have improved to 91% appropriate (185/203; p less than 0.001) had the algorithmic diagnoses dictated management. The superiority of the algorithm was maintained but not bettered in the validation phase. CONCLUSIONS--Applying the algorithm should reduce the morbidity and mortality of neonates with critical heart disease by aiding clinicians in therapeutic decisions for in transit care.

An empirical comparison of expert-derived and data-derived classification trees

Classification trees provide an attractively transparent discrimination technique, and may be derived from both expert opinion and from data analysis. We consider a real and complex problem concerning the diagnosis of babies with suspected critical congenital heart disease into one of 27 classes. A full loss matrix for all possible misclassifications was obtained from clinical assessments. A tree derived from expert opinion was compared with those derived from analysis of 571 past cases, both for the full problem and for a subset of 6 diseases. Automatic methods for tree creation and pruning were found to have problems for rare diseases, and hand-pruning was carried out. Inclusion of costs led to much improved clinical performance, even for trees that had originally been constructed to minimize classification errors. The expert tree showed a specific building strategy that could not be reproduced automatically. The expert tree generally outperformed those derived from data, particularly in the ability to identify important composite features.

Combining clinical judgement and statistical data in expert systems: Over-the-telephone management decisions for critical congenital heart disease in the first month of life

When a new-born baby with congenital heart disease is referred to a regional specialist centre, the transportation management is crucial but must be decided on the basis of clinical information obtained over the telephone. We consider algorithmic and naive statistical approaches to helping in this decision, and on the basis of preliminary results the relative strengths and weaknesses are discussed. A synthesised logical and probabilistic approach appears to have the best potential and could be implemented on hand-held computers.