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PubMed1994

Referral selection bias in the Medicare hospital mortality prediction model: are centers of referral for Medicare beneficiaries necessarily centers of excellence?

OBJECTIVE: Although the Health Care Financing Administration (HCFA) uses Medicare hospital mortality data as a measure of hospital quality of care, concerns have been raised regarding the validity of this concept. A problem that has not been fully evaluated in these data is the potential confounding effect of illness severity factors associated with referral selection and hospital mortality on comparisons of risk-adjusted hospital mortality. We address this issue. DATA SOURCES AND STUDY SETTING: We analyzed the 1988 Medicare hospitalization data file (MEDPAR). We selected data on patients treated at the two Mayo Clinic-associated hospitals in Rochester, Minnesota, and a group of seven other hospitals that treat many patients from large geographic areas. These hospitals have had observed mortality rates substantially lower than those predicted by the HCFA model for the period 1987-1990. STUDY DESIGN: Using the multiple logistic regression model applied by HCFA to the 1988 data, we evaluated the relationship between distance from patient residence to the admitting hospital and risk-adjusted hospital mortality. PRINCIPAL FINDINGS: Among patients admitted to Mayo Rochester-affiliated hospitals, residence outside Olmsted County, Minnesota was independently associated with a 33 percent lower 30-day mortality rate (p < .001) than that associated with residence in Olmsted County. When patients at Mayo hospitals were stratified by residence (Olmsted County versus non-Olmsted County), the observed mortality was similar to that predicted for community patients (9.6 percent versus 10.2 percent, p = .26), whereas hospital mortality for referral patients was substantially lower than predicted (5.0 percent versus 7.5 percent, p = < .001). After incorporation of the HCFA risk adjustment methods, distance from patient residence to the hospitals was also independently associated with mortality among the Mayo Rochester-affiliated hospitals and seven other referral center hospitals. CONCLUSIONS: The HCFA Medicare hospital mortality model should be used with extreme caution to evaluate hospital quality of care for national referral centers because of residual confounding due to severity of illness factors associated with geographic referral that are inadequately captured in the extant prediction model.

Aged↗
PubMed1994

In vivo metabolism of butadiene by mice and rats: a comparison of physiological model predictions and experimental data.

1,3-Butadiene (BD), a rodent carcinogen, is metabolized to mutagenic and potentially DNA-reactive epoxides, including butadiene monoepoxide (BMO) and butadiene diepoxide. A physiological model containing five tissue groups (liver, lung, fat, slowly perfused tissues and rapidly perfused tissues) and blood was developed to describe uptake and metabolism of inhaled BD and BMO. Maximal rates for hepatic and pulmonary metabolism of BD and hepatic metabolism of BMO incorporated into the model were extrapolated from in vitro data (Csanády et al., Carcinogenesis, 13, 1143-1153, 1992). Apparent enzyme affinities used in the model were identified to the values measured in vitro. Model stimulations for BD and BMO uptake were compared to results from experiments in which groups of male Sprague-Dawley rats and B6C3F1 mice were exposed to initial concentrations of 50-5000 p.p.m. BD in closed chamber experiments and published data on BMO uptake by rats and mice. Metabolic rate constants extrapolated from in vitro data stimulated both BMO and BD uptake from closed chambers. The Vmax for hepatic metabolism of BD extrapolated from in vitro studies was 62 mumol/kg/h for rats and 340 mumol/kg/h for mice, while the Vmax for pulmonary metabolism of BD was 1.0 and 22 for rats and mice, respectively. These results demonstrate the usefulness of data derived in vitro for predicting in vivo behavior. Model simulations were also conducted in which only hepatic metabolism of BD was incorporated. These simulations underestimated BD uptake for mice, but not rats. Inclusion of in vitro-derived rates of pulmonary metabolism of BD into the model improved the fit to the data for mice. Since mice, but not rats, develop lung tumors after exposure to BD, these results point to the need for further characterize the metabolic capacity and target cells in the lung for BD and its metabolites. Once characterized, these models can be extended to predict in vivo behavior of BD in humans.

Animals↗
PubMed1985

Comparison between pore model predictions and sheep lung fluid and protein transport.

The multiple pore model of T. R. Harris and R. J. Roselli (1981, J. Appl. Physiol: Respir. Environ. Exercise Physiol. 50, 1-14), was used to simulate lung lymph flow and protein transport at various levels of microvascular pressure. Response of the three-pore structure determined in that study was found to be in excellent agreement with the experimental sheep lung lymph measurements of R. E. Parker, R. J. Roselli, T. R. Harris, and K. L. Brigham (1981, Circ. Res. 49, 1164-1172). Optimal one- and two-pore model structures were also determined and their responses compared with the experimental data. The two-pore model behavior was found to be very similar to that of the three-pore model but a homoporous model which reproduced the experimental findings could not be found. All simulations required interstitial fluid pressure to increase as microvascular pressure was elevated. True filtration-independent conditions could only be simulated when lung vascular pressures were raised to physiologically unrealistic values.

Animals↗
PubMed1989

Assessment of milk transfer coefficients for use in prediction models of radioactivity transport.

The transfer coefficient (Fm) which is widely used to predict the fraction of ingested radionuclides found in milk is an important parameter for modelling. The accuracy of estimates can be improved by considering the following factors that influence the Fm; (1) the physical-chemical form of the radionuclide in the feed of cows, (2) the hay to concentrate ratio of cows diets and (3) correcting for a steady state situation for feed intake and milk concentrations. Factors such as stable element intake, soil intake, milk production rate, metabolic rate and inhalation do not appear to have significant effects upon the transfer coefficient.

Accidents↗
PubMed1985

Clinical validation of a predictive modeling equation for sodium.

Changes in plasma sodium (Na) concentration during hemodialysis were predicted by changes in Na concentration of the dialysate at equilibrium with the plasma, according to the formula C't = CD - (CD - C'0) [(V0 - QFt)/V0]A/QF, where C'0 and C't are the Na concentration of the dialysate at equilibrium with the plasma at times 0 and t, respectively; QF is the ultrafiltration flow rate; V0 is the initial total body water; and CD is the Na dialysate concentration. This modeling involves only one parameter, A, which is the effective sodium dialysance and depends on the dialyzer, the QF, the plasma water flow rate, and the actual Donnan coefficient. Parameter A was evaluated after 1 h of dialysis. Seven routine 4-h dialysis sessions were performed in which the Na concentration of dialysate at equilibrium with the plasma was measured at varying times. The mean (+/- SEM) difference between predicted and measured values was delta C = 0.5 +/- 0.2 mmol/L. These data support the validity of the model that allows the monitoring of Na dialysate concentration to obtain a prescribed Na plasma concentration at the end of a dialysis session.

Humans↗
PubMed1994

Computer-assisted modeling, prediction, and multifactor optimization in micellar electrokinetic chromatography of ionizable compounds.

Previously, the use of phenomenological models to describe the migration behavior of acidic solutes in micellar electrokinetic chromatography (MEKC) was reported. In this paper, the phenomenological approach is further extended by including both acidic and basic solutes and simultaneously taking two important experimental factors (pH and micelle concentration) into consideration. In addition, a general method is described to model the migration behavior of ionizable (both acidic and basic) solutes in MEKC with anionic and cationic micelles. The practical implication of the phenomenological approaches is that they will provide quantitative relationships between solute migration and experimental factors such that the migration behavior can be predicted on the basis of a few initial experiments and that physicochemical parameters of solutes can also be estimated from model fitting. Through computer-assisted modeling, migration behavior of several acidic and basic solutes over a pH-micelle concentration factor space was successfully predicted on the basis of only five experiments. Furthermore, this phenomenological approach was used to predict the separation of a group of aromatic amines in MEKC with anionic micelles, which resulted in a successful separation of 18 aromatic amines in less than 15 min.

Chemistry Techniques, Analytical↗
PubMed1986

Cat lung hemodynamics: comparison of experimental results and model predictions.

Commonly, attempts have been made to learn about the structure and function of the pulmonary vascular bed from measurements of arterial and venous pressures and blood flow rate under steady-state conditions (e.g., from pressure vs. flow data) or dynamic conditions (e.g., from vascular occlusion data). Zhuang et al. (J. Appl. Physiol. 55: 1341-1348, 1983) have presented a detailed model of steady-state cat lung hemodynamics based on direct measurements of anatomical and elasticity data. This model provides an opportunity to better understand the information content of the hemodynamic data. Therefore, in the present study we carried out a series of steady-state and dynamic experiments on isolated cat lungs. We then compared the results with those predicted by the model. We found that the model provided a good fit to the steady-state data. However, to fit the dynamic data, some modifications were necessary to account for the viscous behavior of the vessel walls and to move the first moment of the distribution of vascular resistance toward the arterial end of the vascular bed relative to that of the distribution of vascular compliance. Due to the sensitivity of the vascular resistance to small changes in vessel diameters and branching ratio, the modifications in morphometry represent small changes in morphometric data and are probably within the range of uncertainty in such data. The modifications had little effect on the steady-state model simulations but substantially improved the dynamic model simulations, suggesting that the dynamic data are quite sensitive to small changes in the relative distributions of vessel diameters and elasticity.

Animals↗
PubMed1989

A predictive model for combined temperature and water activity on microbial growth during the growth phase.

An empirical and generalized model is presented, based on a modified Arrhenius equation, for predicting the combined effect of temperature and water activity on the growth rate of bacteria. When it was applied to seven separate sets of wide ranging published results, spanning some 50 years and including a spore-former and a silage micro-organism, predictions explained between 92.9 and 99.0% of the variation in the results with an overall mean of 96.6%. Advantages over existing models are that it is relatively easy to fit to data using least squares regression and requires only five coefficients. These, together with its simplicity and demonstrated wide application, will facilitate its practical use.

Bacteria↗
PubMed1986

Grading scores and survivorship functions in liver cirrhosis: a comparative statistical analysis of various predictive models.

In a group of followed-up liver cirrhotics we evaluated the reliability of prognostic estimates predicted on the basis of a previously described multivariate statistical model (MSM). In the same subjects we also compared theoretical survival estimates obtained by fitting some other liver cirrhosis grading scores (Child-Turcotte's, McCormick's and Orrego's) to prognostic purposes. No statistical difference between actual and MSM-estimated survivorship functions was found (employing a life-table method with Logrank test), thus confirming the prognostic reliability of this multivariate classification model. Such a global and prognosis-correlated index may be recommendable both for comparing different groups of patients, and for assessing treatment effectiveness. Or results also substantially confirm the other investigated classificative methods such as reliable liver cirrhosis severity indexes, although their use for prognostic purposes seems to be less suitable.

Actuarial Analysis↗
PubMed1993

A predictive model for the combined effect of pH, sodium chloride and storage temperature on the growth of Brochothrix thermosphacta.

Growth of Brochothrix thermosphacta was observed under ranges of pH (5.6-6.8), NaCl (0.5-8.0% w/v) and incubation temperature (1-30 degrees C). In order to compare different approaches, two models were used to fit growth curves to viable count data, and to calculate parameters from those fitted curves. Growth responses as a function of pH, NaCl and temperature were described with a quadratic function which was then used to predict growth within the limits where growth was observed. The predictions of the model show good agreement with published observations from other laboratories.

Cell Division↗
PubMed1985

Importance of glucose per se to intravenous glucose tolerance. Comparison of the minimal-model prediction with direct measurements.

Glucose disappearance after an oral or intravenous challenge is a function of the effects of both endogenously secreted insulin and of glucose itself. We previously introduced the term "glucose effectiveness," or SG, defined as the ability of glucose per se to enhance its own disappearance independent of an increment in plasma insulin. The present investigation, performed in conscious dogs, was undertaken to quantify this glucose effect by minimal-model-based analysis of insulin and glucose dynamics after a frequently sampled intravenous glucose tolerance test (FSIGT). The values from the standard FSIGT were then compared with direct measurements obtained from experiments in which the dynamic insulin response to glucose was suppressed with somatostatin (SRIF). In addition, we examined SG values from the modified FSIGT protocol, which involves both glucose and tolbutamide injections. Protocol l (N = 9): FSIGTs were performed and the glucose and insulin data were analyzed by computer. KG was 2.65 +/- 0.28 min-1, S1 was 4.09 +/- 0.34 X 10(4) min-1/(microU/ml), and SG was 0.033 +/- 0.004 min-1. Protocol II (N = 6): FSIGTs were performed on animals in which SRIF was infused (0.8 micrograms/min X kg) to obliterate the dynamic insulin response to glucose injection. Before the FSIGt, insulin and glucagon were infused intraportally to reattain basal glycemia. Without dynamic insulin, KG was reduced to 0.96 +/- 0.18 min-1 (P less than 0.0001). However, SG, estimated from the exponential rate of fall of plasma glucose in the absence of dynamic insulin, was similar to the standard FSIGTs: 0.025 +/- 0.004 (P greater than 0.25). Protocol III (N = 6): modified FSIGTs were performed using glucose and tolbutamide injections for a better estimate of model parameters. Model parameters Sl and SG, and the KG were not different from standard FSIGTs (P greater than 0.3). In fact, the value of SG (0.028 +/- 0.003 min-1) was nearly identical to the direct measure from protocol II. Therefore, the effect of glucose per se on glucose decline, estimated by modeling the standard and modified FSIGTs, was confirmed by a direct measurement with the endogenous insulin response suppressed with SRIF. Also, the time course of the insulin effect to enhance net glucose disappearance from plasma [Ieff(t)] was calculated from the data of protocol II, and was the same as the time course predicted by the model. These studies demonstrate the ability of the computer modeling approach to separate insulin-dependent and glucose-dependent glucose disappearance, and represent a direct confirmation of the minimal model.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗
PubMed1982

Prediction of remission in adult acute leukemia: development and testing of predictive models.

Logistic regression methods were applied to derive a set of models relating achievement of CR to prognostic characteristics in a group of 300 adult acute leukemia patients treated with cytosine arabinoside, vincristine, and prednisone combined with adriamycin (ADOAP) or rubidazone (ROAP). These models were tested prospectively in an independent group of 107 subsequent patients treated with ADOAP or ROAP therapy, by comparing observed outcomes to predictions of response based on the models. Several models were able to identify subgroups of patients with good, intermediate, and poor prognoses. A model regarded as clinically useful and which provided a good fit to both the population from which it was derived and the test population included the pretreatment factors age, history of an antecedent hematologic disorder, temperature, blood urea nitrogen, hemoglobin, and liver size.

Acute Disease↗