Does Every Calculation Formula Fit for All Types of Intraocular Lenses? Optimization of Constants for Tecnis ZA9003 and ZCB00 Is Necessary

Background and Objectives: To evaluate the performance of intraocular lenses (IOLs) using power calculation formulas on different types of IOL. Materials and Methods: 120 eyes and four IOL types (BioLine Yellow Accurate Aspheric IOL (i-Medical), TECNIS ZCB00, TECNIS ZA9003 (Johnson & Johnson) (3-piece IOL) and Softec HD (Lenstec)) were analyzed. The performance of Haigis, Barret Universal II and SKR-II formulas were compared between IOL types. The mean prediction error (ME) and mean absolute prediction error (MAE) were analyzed. Results: The overall percentage of eyes predicted within ±0.25 diopters (D) was 40.8% for Barret; 39.2% Haigis and 31.7% for SRK-II. Barret and Haigis had a significantly lower MAE than SRK-II (p < 0.05). The results differed among IOL types. The largest portion of eyes predicted within ±0.25 D was with the Barret formula in ZCB00 (33.3%) and ZA9003 (43.3%). Haigis was the most accurate in Softec HD (50%) and SRK-II in Biolline Yellow IOL (50%). ZCB00 showed a clinically significant hypermetropic ME compared to other IOLs. Conclusions: In general, Barret formulas had the best performance as a universal formula. However, the formula should be chosen according to the type of IOL in order to obtain the best results. Constant optimizations are necessary for the Tecnis IOL ZCB00 and ZA9003, as all of the analyzed formulas achieved a clinically significant poor performance in this type of IOL. ZCB00 also showed a hypermetropic shift in ME in all the formulas.

Lenstar LS 900 versus Pentacam-AXL: analysis of refractive outcomes and predicted refraction

Analysis of refractive outcomes, using biometry data collected with a new biometer (Pentacam-AXL, OCULUS, Germany) and a reference biometer (Lenstar LS 900, HAAG-STREIT AG, Switzerland), in order to assess differences in the predicted and actual refraction using different formulas. Prospective, institutional study, in which intraocular lens (IOL) calculation was performed using the Haigis, SRK/T and Hoffer Q formulas with the two systems in patients undergoing cataract surgery between November 2016 and August 2017. Four to 6 weeks after surgery, the spherical equivalent (SE) was derived from objective refraction. Mean prediction error (PE), mean absolute error (MAE) and the median absolute error (MedAE) were calculated. The percentage of eyes within ± 0.25, ± 0.50, ± 1.00, and ± 2.00 D of MAE was determined. 104 eyes from 76 patients, 35 males (46.1%), underwent uneventful phacoemulsification with IOL implantation. Mean SE after surgery was − 0.29 ± 0.46 D. Mean prediction error (PE) using the SRK/T, Haigis and Hoffer Q formulas with the Lenstar was significantly different (p > 0.0001) from PE calculated with the Pentacam in all three formulas. Percentage of eyes within ± 0.25 D MAE were larger with the Lenstar device, using all three formulas. The difference between the actual refractive error and the predicted refractive error is consistently lower when using Lenstar. The Pentacam-AXL user should be alert to the critical necessity of constant optimization in order to obtain optimal refractive results.

Anterior chamber depth, lens thickness and intraocular lens calculation formula accuracy: nine formulas comparison

Background/AimsTo investigate the influence of anterior chamber depth (ACD) and lens thickness (LT) on 9 intraocular lens (IOL) power calculation formulas accuracy, in patients with normal axial lengths.MethodsRetrospective case series, including patients having uncomplicated cataract surgery with insertion of a single IOL model, divided into three groups according to preoperative ACD. Each group was further subdivided into three subgroups, according to the LT. Using optimised constants, refraction prediction error was calculated for Barrett Universal II, Emmetropia Verifying Optical (EVO) V.2.0, Haigis, Hill-RBF V.2.0, Hoffer Q, Holladay 1, Kane, PEARL-DGS and SRK/T formulas. Mean prediction error, mean and median absolute error (MedAE) and the percentage of eyes within ±0.25D, ±0.50D and ±1.00D were also calculated.ResultsThe study included 695 eyes from 695 patients. For ACD ≤3.0 mm and ≥3.5 mm, mean prediction error of SRK/T, Hoffer Q and Holladay 1 was significantly different from 0 (p<0.05). PEARL-DGS, Kane, EVO V.2.0 and Barrett Universal II were more accurate than the Hoffer Q in ACD ≤3.0 mm (p<0.05). Kane, PEARL-DGS, EVO V.2.0 and Barrett Universal II revealed the lowest variance of mean and MedAE by ACD and LT subgroup. Haigis and Hill-RBF V.2.0 were significantly influenced by LT, independently of the ACD, with a myopic shift with thin lenses and a hyperopic shift with thick lenses (p<0.05).ConclusionNew generation formulas, particularly Kane, PEARL-DGS and EVO V.2.0, seem to be more reliable and stable even in eyes with extreme ACD-LT combinations.

External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was −2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

Prediction of Metabolizable Energy Concentrations of Herbage in the Qinghai–Tibetan Plateau Using Tibetan Sheep Digestibility Data

Due to its extremely harsh environment, including high altitude, hypoxia, long cold season, and strong ultraviolet radiation in the Qinghai–Tibet Plateau (QTP), herbage species and nutritional value of the pasture may differ considerably from elsewhere across the world. The aim of the present study was to develop biologically relevant equations for estimating the metabolizable energy (ME) value of fresh native herbages in the QTP using digestibility variables and chemical concentrations in the herbage offered to Tibetan sheep at the maintenance level. A total of 11 digestibility trials (6 sheep/trial) were performed in different grazing seasons from 2011 to 2016. The herbage was harvested daily in the morning and offered to sheep at the maintenance feeding level. Thirty-seven equations were developed for the prediction of herbage digestible energy (DE) and ME energy values. The mean prediction error for ME was the lowest when using herbage gross energy digestibility as a sole predictor. When using other digestibility variables (e.g., dry matter and organic matter) as primary predictors, addition of herbage nutrient concentration reduced the difference between predicted and actual values. When DE was used as the primary explanatory variable, mean prediction error was reduced with the addition of ash, nitrogen (N), diethyl ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) concentrations. The internal validation of the present equations showed lower prediction errors when compared with those of existing equations for prediction of DE and ME concentrations in the herbage. Equations developed in the current study may thus allow for an improved and accurate prediction of metabolizable energy concentrations of herbage in practice, which is critical for the development of sustainable grazing systems in the QTP.

Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method

Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of predictive accuracy between the Sawchuk–Zaske and Bayesian methods in Japanese cancer patients is not completely understood. Objective To retrospectively compare the accuracy of predicting vancomycin concentrations between the Sawchuk–Zaske method and the Bayesian method in Japanese cancer patients. Methods Using data from 48 patients with various malignancies, the predictive accuracy (bias) and precision of the two methods were assessed by calculating the mean prediction error, the mean absolute prediction error, and the root mean squared prediction error. Results Prediction of the trough and peak vancomycin concentrations by the Sawchuk–Zaske method and the peak concentration by the Bayesian method showed a bias toward low values according to the mean prediction error. However, there were no significant differences between the two methods with regard to the changes of the mean prediction error, mean absolute prediction error, and root mean squared prediction error. Conclusion The Sawchuk–Zaske method and Bayesian method showed similar accuracy for predicting vancomycin concentrations in Japanese cancer patients.

P112 Prediction of free Ceftriaxone concentration in children: disease and maturation do matter

BackgroundTo evaluate the predictive ability of the existing formula to measure free ceftriaxone levels in children, and optimize the formula by adding disease and maturation factors.MethodsFifty children receiving ceftriaxone were evaluated, and the predictive performance of the different equations were assessed by mean absolute error (MAE), mean prediction error (MPE) and linear regression of predicted vs. actual free levels.ResultsThe average free ceftriaxone concentration was 2.11 ± 9.51µg/ml. The predicted free concentration was 1.15 ± 4.39µg/ml with the in vivo binding equation, which increased to 1.58 ± 7.73µg/ml and 2.01 ± 9.53µg/ml when adjusted for age (disease adapted equation), and age and albumin (disease-maturation equation) respectively. The average MAE values were 0.48 (in vivo banding equation), 0.34 (disease adapted equation) and 0.41 (disease maturation equation). The average MPE values were -0.41 (in vivo binding equation), 0.14 (disease adapted equation) and 0.09 (disease maturation equation). The respective linear regression equations and coefficients were y=1.8647x+1.0731(R2=0.7398), y=1.1455x+0.8414(R2=0.8674), and y=0.9664x(R2=0.8641) for the in vivo binding, disease adapted and disease maturation equations respectively.ConclusionCompared to the in vivo binding equation, the disease adapted and disease maturation equations showed lower MAE and MPE values, and the latter showed the lowest MPE value. In addition, the slope of the disease maturation equation was closer to 1 compared to the other two. Therefore, the optimized disease maturation equation should be used to measure free ceftriaxone levels in children.Disclosure(s)Nothing to disclose.

Fat mass estimation in neonates: anthropometric models compared with air displacement plethysmography

Newborn adiposity, a nutritional measure of the maternal–fetal intra-uterine environment, is representative of future metabolic health. An anthropometric model using weight, length and flank skinfold to estimate neonatal fat mass has been used in numerous epidemiological studies. Air displacement plethysmography (ADP), a non-invasive technology to measure body composition, is impractical for large epidemiological studies. The study objective was to determine the consistency of the original anthropometric fat mass estimation equation with ADP. Full-term neonates were studied at 12–72 h of life with weight, length, head circumference, flank skinfold thickness and ADP measurements. Statistical analyses evaluated three models to predict neonatal fat mass. Lin’s concordance correlation coefficient, mean prediction error and root mean squared error between the predicted and observed ADP fat mass values were used to evaluate the models, where ADP was considered the gold standard. A multi-ethnic cohort of 468 neonates were studied. Models (M) for predicting fat mass were developed using 349 neonates from site 1, then independently evaluated in 119 neonates from site 2. M0 was the original anthropometric model, M1 used the same variables as M0 but with updated parameters and M2 additionally included head circumference. In the independent validation cohort, Lin’s concordance correlation estimates demonstrated reasonable accuracy (model 0: 0·843, 1: 0·732, 2: 0·747). Mean prediction error and root mean squared error in the independent validation was much smaller for M0 compared with M1 and M2. The original anthropometric model to estimate neonatal fat mass is reasonable for predicting ADP, thus we advocate its continued use in epidemiological studies.

Can a bottom-moored echo sounder array provide a survey-comparable index of abundance?

A small number of stationary echo sounders have the potential to produce abundance indices where fish repeatedly occupy localized areas (e.g., spawning grounds). To investigate this possibility, we deployed three trawl-resistant moorings with a newly designed autonomous echo sounder for ∼85 days during the walleye pollock (Gadus chalcogrammus) spawning season in Shelikof Strait, Alaska. Backscatter observed from the moorings was highly correlated with ship-based acoustic surveys, suggesting that the mooring observations reflect abundance over much larger areas than the observation volume of the acoustic beam. A retrospective analysis of a 19-year time series of prespawning walleye pollock surveys was used to select mooring locations and determine that three to five moorings can produce an index of walleye pollock backscatter comparable to that produced by a ship-based survey covering ∼18 000 km2 (mean prediction error of <11% for five moorings). The three moorings deployed in Shelikof Strait yielded a backscatter estimate that was within ∼15%–20% of that observed during the survey. Thus, it appears feasible to design a relatively sparse mooring array to provide abundance information and other aspects of fish behavior in this environment.

Minimizing Crop Damage Through Understanding Relationships Between Pyrethrum Phenology and Ray Blight Disease Severity

The most damaging foliar disease of pyrethrum in Australia is ray blight caused by Stagonosporopsis tanaceti. The probability of growers incurring economic losses caused by this disease has been substantially reduced by the implementation of a prophylactically applied spring fungicide program. This has been traditionally initiated when 50% of the stems have reached between 5 and 10 cm in height. Data collected on the emergence of stems from semidormant plants over late winter from 27 fields across northern Tasmania from 2009 to 2011 were used to develop a degree-day model to assist with initiation of the fungicide program. Temporal changes in cumulative proportion of plants with elongated stems were well described by a logistic growth model (R2 ≥ 0.97 across all fields). These models were used to calculate the number of days until 50% of the sampling units had at least one elongated stem for the calculation of simple degree-days, assuming a nominal biofix date of the austral winter solstice. The median date for 50% stem elongation was estimated as 30 August in these data sets. Mean error and root mean square error of degree-day models were minimized when a base of 0°C was selected. Mixed-model analysis found prediction errors to be significantly affected by geographic region, requiring the use of scalar correction factors for specific production regions. In the Western region, 50% stem emergence was predicted at 590.3 degree-days (mean prediction error = 0.7 days), compared with 644.6 (mean prediction error = 7.7 days) in the Coastal region and 684.7 (mean prediction error = 0.7 days) degree-days in the Inland region. The importance of fungicide timing for initiation of the spring disease management program in minimizing losses (expressed as percent disease control in October) was also quantified. This relationship was best explained by a split-line regression with a significant break-point of 513.8 degree-days, which corresponded to 10.7% of sampling units with elongated stems. Overall, this research indicated that disease management may be improved by applying the first fungicide of the program substantially earlier in phenological development of the stems than currently recommended.