Risk-adjusted monitoring of binary surgical outcomes.

A graphical procedure suitable for prospectively monitoring surgical performance is proposed. The approach is based on accumulating evidence from the outcomes of all previous surgical patients in a series using a new type of cumulative sum chart. Cumulative sum procedures are designed to "signal" if sufficient evidence has accumulated that the surgical failure rate has changed substantially. In this way, the chart rapidly detects deterioration (or improvement) in surgical performance while not overreacting to the expected fluctuations due to chance. Through the use of a likelihood-based scoring method, the cumulative sum procedure is adapted so that it adjusts for the surgical risk of each patient estimated preoperatively. The procedure is therefore applicable in situations where it is desirable to adjust for a mix of patients. Signals of the chart lead to investigations of the cause and to the timely introduction of remedial measures designed to avoid unnecessary future failures.

Monitoring surgical performance using risk-adjusted cumulative sum charts.

The cumulative sum (CUSUM) procedure is a graphical method that is widely used for quality monitoring in industrial settings. More recently it has been used to monitor surgical outcomes whereby it 'signals' if sufficient evidence has accumulated that there has been a change in the surgical failure rate. A limitation of the standard CUSUM procedure in this context is that since it is simply based on the observed surgical outcomes, it may signal as a result of changes in the referral pattern, such as an increased proportion of high-risk patients, rather than due to a change in the actual surgical performance. We describe a new CUSUM procedure that adjusts for each patient's pre-operative risk of surgical failure through the use of a likelihood-based scoring method. The procedure is therefore ideally suited for settings where there is a variable mix of patients over time.

Monitoring paired binary surgical outcomes using cumulative sum charts.

Correlated binary data are encountered in many areas of medical research, system reliability and quality control. For monitoring failures rates in such situations, simultaneous bivariate cumulative sum (CUSUM) charts with the addition of secondary control limits are proposed. Using an approach based on a Markov chain model, the run length properties of such a monitoring scheme can be determined for sudden, or gradual, changes in the failure rates. The proposed control charts are easy to implement, and are shown to be very effective at detecting small changes in the rate of undesirable outcomes, especially when the changes are gradual. This procedure is illustrated using bivariate outcome data arising from a series of paediatric surgeries. The methodology is sufficiently general that it may be adapted for multivariate normal, binomial or Poisson responses.

Adipose tissue distribution and chemical structure of basic lipophilic drugs: desipramine, N-acetyl desipramine, and haloperidol.

Single-dose intravenous injections of desipramine to rats resulted in a distribution pattern typical of basic lipophilic drugs, i.e., highest concentrations in lung and lowest in adipose tissue and plasma. In contrast, after N-acetyldesipramine, a non-basic analogue of desipramine with comparable lipophilicity, concentrations of this drug in adipose tissue were much higher than in lean tissue or plasma as a result of redistribution into the former and rapid disappearance from the latter tissue. N-Acetyldesipramine had much lower plasma and tissue half-lives than desipramine, but at the same time a much higher adipose/plasma concentration ratio and adipose storage index. Chronic administration of the basic lipophilic drug, haloperidol, to rats in their diet over 21 days resulted in a steady-state distribution pattern with highest concentrations in lung and lowest concentrations without accumulation in adipose tissue. This study provides additional evidence for the influence of basic groups on the distribution of lipophilic drugs. Thus, basic lipophilic drugs do not undergo redistribution into adipose tissues, possibly because of a competition by stronger binding to lean tissue as a result of lysosomotropism.

Kinetics of distribution and adipose tissue storage as a function of lipophilicity and chemical structure. II. Benzodiazepines.

Distribution kinetics of flurazepam, medazepam, prazepam, and clobazam were determined in rats. Concentration-time curves of unchanged drugs and non-polar metabolites in plasma, adipose tissue, liver, brain, and muscle after a single iv administration were obtained using GLC/electron capture detector analysis. Pharmacokinetic parameters were calculated for plasma and tissues. Adipose tissue storage was quantified with the adipose storage index (ASI). Including data of benzodiazepines from the literature, a correlation between ASI and log P (over a range 1.6-3.8) was nonexistent and the influence of individual functional groups was not easily discernible. However, benzodiazepines with a pKa (base) within the range 1.6-6.2 were stored in adipose tissue (ASI greater than 1), whereas those with pKa greater than 7 were not (ASI less than 1). Since many other basic lipophilic drugs with pKa greater than 7 are not stored in adipose tissue, and this is likely due to lysosomal trapping in lean tissues, which requires a pKa value above 7, it is suggested that within the series of benzodiazepines, adipose tissue storage is mainly influenced by the basicity of the drugs.

Kinetics of distribution and adipose tissue storage as a function of lipophilicity and chemical structure. I. Barbiturates.

Distribution kinetics of 5-ethyl-substituted oxy-, N-alkyl-, and thiobarbiturates covering a range of partition coefficients of octanol/water (log P 1.6 to 4.1) were determined in rats. Concentration-time curves for plasma, adipose tissue, liver, and muscle after single iv administration were obtained using HPLC analysis. Pharmacokinetic parameters were calculated for plasma and tissues. A physiological pharmacokinetic model allowed the simulation and prediction of adipose tissue kinetics based on blood and plasma kinetics, adipose tissue/plasma distribution coefficient, volume and perfusion rate of adipose tissue. Adipose tissue storage was quantified with the adipose storage index (ASI). Including data of barbiturates from the literature, the correlation between ASI and log P was poor except for oxybarbiturates not substituted in N1. Within comparable log P ranges, ASI values increased from oxy- to N-alkylated to thiobarbiturates. Thus, even within the chemical class of barbiturates log P is not a sufficient criterion for adipose tissue storage. Rather, adipose tissue storage is influenced by individual functional groups.