Density estimation of a biomedical variable subject to measurement error using an auxiliary set of replicate observations

Artifact Processing Methods Influence on Intraoperative Hypotension Quantification and Outcome Effect Estimates

BACKGROUND

Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering.

METHODS

The authors performed a systematic literature search to identify artifact filtering methods. Subsequently, these methods were applied to the data of anesthesia procedures with invasive blood pressure monitoring. Different hypotension measures were calculated (i.e., presence, duration, maximum deviation below threshold, and area under threshold) across different definitions (i.e., thresholds for mean arterial pressure of 50, 60, 65, 70 mmHg). These were then used to estimate the association with postoperative myocardial injury.

RESULTS

After screening 3,585 papers, the authors included 38 papers that reported artifact filtering methods. The authors applied eight of these methods to the data of 2,988 anesthesia procedures. The occurrence of hypotension (defined with a threshold of 50 mmHg) varied from 24% with a median filter of seven measurements to 55% without an artifact filtering method, and between 76 and 90% with a threshold of 65 mmHg. Standardized odds ratios for presence of hypotension ranged from 1.16 (95% CI, 1.07 to 1.26) to 1.24 (1.14 to 1.34) when hypotension was defined with a threshold of 50 mmHg. Similar variations in standardized odds ratios were found when applying methods to other hypotension measures and definitions.

CONCLUSIONS

The method of artifact filtering can have substantial effects on estimates of hypotension prevalence. The effect on the association between intraoperative hypotension and postoperative myocardial injury was relatively small. Nevertheless, the authors recommend that researchers carefully consider artifacts handling and report the methodology used.

Density estimation in the presence of heteroscedastic measurement error of unknown type using phase function deconvolution

It is important to properly correct for measurement error when estimating density functions associated with biomedical variables. These estimators that adjust for measurement error are broadly referred to as density deconvolution estimators. While most methods in the literature assume the distribution of the measurement error to be fully known, a recently proposed method based on the empirical phase function (EPF) can deal with the situation when the measurement error distribution is unknown. The EPF density estimator has only been considered in the context of additive and homoscedastic measurement error; however, the measurement error of many biomedical variables is heteroscedastic in nature. In this paper, we developed a phase function approach for density deconvolution when the measurement error has unknown distribution and is heteroscedastic. A weighted EPF (WEPF) is proposed where the weights are used to adjust for heteroscedasticity of measurement error. The asymptotic properties of the WEPF estimator are evaluated. Simulation results show that the weighting can result in large decreases in mean integrated squared error when estimating the phase function. The estimation of the weights from replicate observations is also discussed. Finally, the construction of a deconvolution density estimator using the WEPF is compared with an existing deconvolution estimator that adjusts for heteroscedasticity but assumes the measurement error distribution to be fully known. The WEPF estimator proves to be competitive, especially when considering that it relies on minimal assumption of the distribution of measurement error.

Day-specific probabilities of conception in fertile cycles resulting in spontaneous pregnancies

STUDY QUESTION

When, within the female cycle, does conception occur in spontaneously fertile cycles?

SUMMARY ANSWER

This study provides reference values of day-specific probabilities of date of conception in ongoing pregnancies. The maximum probability of being within a 5-day fertile window was reached on Day 12 following the last menstrual period (LMP).

WHAT IS KNOWN ALREADY

The true date of conception is not observable and may only be estimated. Accuracy of these estimates impacts on obstetric management of ongoing pregnancies. Timing of ovulation and fertility has been extensively studied in prospective studies of non-pregnant fertile women using error-prone proxies, such as hormonal changes, body-basal temperature and ultrasound, yielding day-specific probabilities of conception and fertile windows. In pregnant women, date of conception may be retrospectively estimated from early pregnancy fetal measurement by ultrasound.

STUDY DESIGN, SIZE, DURATION

Retrospective analysis of consecutive pregnancies in women referred for routine first-trimester screening, over a 3-year period (2009-2011) in a single ultrasound center (n = 6323).

PARTICIPANTS/MATERIALS, SETTING, METHODS

Within the overall population, 5830 cases with a certain date of last menses were selected for analysis. The date of conception was estimated using a crown-rump length biometry and an equation derived from IVF/ICSI pregnancies. Day-specific probabilities of conception were estimated across several covariates, including age, cycle characteristics and ethnicity, using deconvolution methods to account for measurement error.

MAIN RESULTS AND THE ROLE OF CHANCE

Overall, the day-specific probability of conception sharply rises at 7 days after the LMP, reaching its maximum at 15 days and returning to zero by 25 days. Older women tend to conceive earlier within their cycle, as did women with regular cycles and white and black women compared with Asian ethnicity. The probability of being within the fertile window was 2% probability at Day 4, a maximum probability of 58% at Day 12 and a 5% probability by Day 21 of the cycle.

LIMITATIONS, REASONS FOR CAUTION

Although conception is believed to occur within hours following ovulation, a discrepancy is theoretically possible. However, when comparing our results to those of prospective studies, no such difference was found. The equation used for estimating the date of pregnancy was estimated in IVF/ICSI pregnancies, which could lead to potential bias in spontaneous pregnancies. However, in our population, the observed bias was negligible. Non-fertile cycles and early pregnancy losses are necessarily overlooked because of the nature of our data.

WIDER IMPLICATIONS OF THE FINDINGS

Because of the wider access to retrospective data and the potential bias in prospective studies of ovulation monitoring, this study should broaden the perspectives of future epidemiologic research in fertility and pregnancy monitoring.

STUDY FUNDING/COMPETING INTERESTS

None.