Median Absolute Performance Error Research Articles

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models. Long short-term memory (LSTM) and gradient-boosted regression tree (GBRT) models were developed using time-series doses and concentrations of tacrolimus with covariates of age, sex, weight, height, liver enzymes, total bilirubin, international normalized ratio, albumin, serum creatinine, and hematocrit. We conducted performance comparisons with linear regression and populational pharmacokinetic models, followed by external validation using the eICU Collaborative Research Database collected in the United States between 2014 and 2015. In the external validation, the LSTM outperformed the GBRT, linear regression, and populational pharmacokinetic models with median performance error (8.8%, 25.3%, 13.9%, and − 11.4%, respectively; P < 0.001) and median absolute performance error (22.3%, 33.1%, 26.8%, and 23.4%, respectively; P < 0.001). Dosing based on the LSTM model’s suggestions achieved therapeutic concentrations more frequently on the chi-square test (P < 0.001). Patients who received doses outside the suggested range were associated with longer ICU stays by an average of 2.5 days (P = 0.042). In conclusion, machine learning models showed excellent performance in predicting tacrolimus concentration in liver transplantation recipients and can be useful for concentration titration in these patients.

Accuracy of intraocular lens calculation formulas based on swept-source OCT biometer in cataract patients with phakic intraocular lens

PurposeTo research the accuracy of intraocular lens (IOL) calculation formulas and investigate the effect of anterior chamber depth (ACD) and lens thickness (LT) measured by swept-source optical coherence tomography biometer (IOLMaster 700) in patients with posterior chamber phakic IOL (PC-pIOL).MethodsRetrospective case series. The IOLMaster 700 biometer was used to measure axial length (AL) and anterior segment parameters. The traditional formulas (SRK/T, Holladay 1 and Haigis) with or without Wang-Koch (WK) AL adjustment, and new-generation formulas (Barret Universal II [BUII], Emmetropia Verifying Optical [EVO] v2.0, Kane, Pearl-DGS) were utilized in IOL power calculation.ResultsThis study enrolled 24 eyes of 24 patients undergoing combined PC-pIOL removal and cataract surgery at Xiamen Eye Center of Xiamen University, Xiamen, Fujian, China. The median absolute prediction error in ascending order was EVO 2.0 (0.33), Kane (0.35), SRK/T-WKmodified (0.42), Holladay 1-WKmodified (0.44), Haigis-WKC1 (0.46), Pearl-DGS (0.47), BUII (0.58), Haigis (0.75), SRK/T (0.79), and Holladay 1 (1.32). The root-mean-square absolute error in ascending order was Haigis-WKC1 (0.591), Holladay 1-WKmodified (0.622), SRK/T-WKmodified (0.623), EVO (0.673), Kane (0.678), Pearl-DGS (0.753), BUII (0.863), Haigis (1.061), SRK/T (1.188), and Holladay 1 (1.513). A detailed analysis of ACD and LT measurement error revealed negligible impact on refractive outcomes in BUII and EVO 2.0 when these parameters were incorporated or omitted in the formula calculation.ConclusionThe Kane, EVO 2.0, and traditional formulas with WK AL adjustment displayed high prediction accuracy. Furthermore, the ACD and LT measurement error does not exert a significant influence on the accuracy of IOL power calculation formulas in highly myopic eyes implanted with PC-pIOL.

Accuracy of intraocular lens power formulas in patients with average keratometry greater than 46 diopters

ObjectiveTo compare the accuracy of Kane, Barrett Universal II, Haigis, and SRK-T formulas in eyes with average keratometry greater than 46 diopters (D). MethodsA retrospective analysis was conducted on 101 eyes of 101 patients with average keratometry greater than 46 D. The absolute prediction error (EA) was obtained for each patient one month after surgery. The mean absolute prediction error (MEA), median absolute prediction error (MedEA) and the percentage of patients with absolute refractive error less than 0.25 D, 0.50 D, and 1.00 D were calculated for each formula analyzed. ResultsThe Kane formula achieved the lowest MEA (0.53 ± 0.43) and the lowest MedEA (0.41), followed by Barrett Universal II (MEA: 0.56 ± 0.42, MedEA: 0.49), SRK-T (MEA: 0.59 ± 0.44, MedEA: 0.54), and Haigis (MEA: 0.77 ± 0.47, MedEA: 0.69), showing a significant difference in the results. It was also observed that the Kane formula was the most accurate, with the highest percentage of patients, with EA less than 0.25 D, 0.50 D, and 1.00 D (30.7%, 54.4%, and 86.1%, respectively), while the Haigis formula was the least accurate (12.9%, 33.7%, and 69.3%, respectively). ConclusionIn eyes with corneas having average keratometry greater than 46 D, the Kane formula proves to be a useful tool in intraocular lens (IOL) power calculation and demonstrates higher precision compared to the Barrett Universal II, SRK-T, and Haigis formulas.

Fellow eye data for intraocular lens calculation in eyes undergoing combined phacovitrectomy.

To evaluate whether the intraocular lens (IOL) calculation of the fellow eye (FE) can be used in eyes undergoing combined phacovitrectomy. In this retrospective, consecutive case series, we enrolled patients who underwent combined phacovitrectomy with silicone oil removal and IOL implantation at the Goethe-University. Preoperative examinations included biometry (IOLMaster 700; Carl Zeiss). We used the IOL calculation of the FE (FE group) to calculate the prediction error compared with the IOL calculation using only the axial length (AL) of the FE (AL-FE group), as well as using the AL of the operated eye (OE group) in addition to the measurable biometric parameters. IOL calculation was performed using the Barrett Universal II formula. We compared the mean (MAE) and median absolute prediction error (MedAE) with each other. Furthermore, the number of eyes with ±0.50, ±1.00 and ±2.00 dioptres (D) deviation from the target refraction was compared. In total, 79 eyes of 79 patients were included. MedAE was lowest in the OE group (0.41 D), followed by FE group (1.00 D) and AL-FE group (1.02 D). Comparison between the AL-FE and FE groups showed no statistically significant difference (p = 0.712). Comparing eyes within ±0.50 D of the target refraction, the OE group (63.3%) performed best, followed by the AL-FE group (27.8%) and the FE group (26.6%). Our results indicate no clinically relevant difference between using the IOL calculation of the FE versus using only the AL of the FE in addition to the measurable parameters for the IOL calculation. A two-step procedure should always be strived for.

IOL calculation using six formulas in children undergoing lens extraction and primary IOL implantation with and without posterior optic capture.

To evaluate formulas for intraocular lens (IOL) calculation in children undergoing lens extraction and IOL implantation. Retrospective, consecutive case series at the Department of Ophthalmology, Goethe University Frankfurt, Germany. We included eyes that received lens extraction and IOL implantation (SN60AT, Alcon, Fort Worth, TX) due to congenital or juvenile cataract. Preoperative assessments included biometry (IOLMaster 500/700, Carl Zeiss Meditec, Germany). To evaluate the measurements, we compared the mean prediction error (MPE), mean and median absolute prediction error (MAE, MedAE) of six different formulas, and number of eyes within ± 0.5, ± 1.0, ± 2.0D of target refraction. Postoperative spherical equivalent was measured by retinoscopy 4-12 weeks after surgery. 66 eyes matched our inclusion criteria with a mean age of 6.3 years ± 3.2. MedAE was lowest in SRK/T (0.55D ± 1.08) followed by Holladay I (0.75D ± 1.00), EVO 2.0 (0.80D ± 0.89), Barrett Universal II (BUII, 0.86D ± 1.00), Hoffer Q (0.97 D ± 0.94), and Haigis (1.10D ± 0.95). Regarding eyes within ± 0.5D SRK/T (45.5.%, 30 eyes) performed best, followed by Holladay I (36.4%, 24 eyes), EVO 2.0 and BUII (each 34.8%, 23 eyes). There was a myopic shift seen in all formulas (MPE: -0.21 to -0.90D). Using modern formulas, or even AI formulas, for IOL calculation in children's eyes does barely improve predictability of the postoperative refraction. A myopic shift can be found for all formulas. However, specific formulas like SRK/T seem to better anticipate this.

Accuracy of using the axial length of the fellow eye for IOL calculation in retinal detachment eyes undergoing silicone oil removal

PurposeEvaluate whether the axial length of the fellow eye can be used to calculate the intraocular lens (IOL) in eyes with retinal detachment.DesignRetrospective, consecutive case series.MethodsOur study was conducted at...

Dose-Response Study of Norepinephrine Infusion for Maternal Hypotension in Preeclamptic Patients Undergoing Cesarean Delivery Under Spinal Anesthesia.

Spinal anesthesia remains the preferred mode of anesthesia for preeclamptic patients during cesarean delivery. We investigated the incidence of maternal hypotension under spinal anesthesia during cesarean delivery, by comparing different prophylactic infusion rates of norepinephrine with normal saline. We randomly allocated 180 preeclamptic patients (45 in each groups) aged 18-45 scheduled for cesarean delivery to receive one of four prophylactic norepinephrine infusions at doses of 0 (normal saline group), 0.025 (0.025 group), 0.05 (0.05 group), or 0.075 (0.075 group) µg/kg/min following spinal anesthesia. The primary endpoint was the incidence of maternal hypotension (systolic blood pressure < 80% of baseline). The incidence of maternal hypotension was reduced with different prophylactic infusion rates of norepinephrine (26.7%, 15.6%, and 6.7%) compared with normal saline (37.8%) with a significant decreasing trend (p = 0.002). As the infusion doses of norepinephrine increased, there is a significant decreasing trend in deviation of systolic blood pressure control (median performance error; median absolute performance error) from baseline (p < 0.001; p < 0.001) and need for rescue norepinephrine boluses (p = 0.020). The effective dose 50 and effective dose 90 of prophylactic norepinephrine infusion were - 0.018 (95% confidence interval - 0.074, 0.002) µg/kg/min and 0.065 (95% confidence interval 0.048, 0.108) µg/kg/min, respectively. Prophylactic infusion of norepinephrine, as compared to no preventive measures, can effectively reduce the incidence of maternal hypotension in preeclamptic patients under spinal anesthesia during cesarean delivery, without increasing other adverse events for either the mother or neonate. Clinical trials.gov identifier number NCT04556370.

Comparison of Different Intraocular Lens Power Calculation Formulas in Eyes With Primary Angle Closure.

Prospective case series. This prospective study determines which formulas can best predict the refractive outcome in patients with primary angle closure disease (PACD) after cataract surgery. To compare the accuracy of 6 intraocular lens power calculation formulas, Barrett Universal II (BU II), Haigis, Hoffer Q, Holladay I, Kane and SRK/T, in eyes with PACD. Patients diagnosed with PACD and cataracts and who met the indication for cataract surgery were enrolled in the study. Six intraocular lens power calculation formulas were used to calculate the refractive diopter. The percentage of eyes with prediction error (PE) within ±0.50 D and the median absolute PE were compared to determine the accuracy of different formulas in patients with PACD. Subgroup analysis was performed according to axial length (AL). The accuracy of BU II was compared between patients with PACD and patients with age-related cataracts. One hundred five patients (105 eyes) with PACD and 35 patients (35 eyes) with age-related cataracts were enrolled in the study. Haigis, Kane, and BU II formula achieved a comparable outcome and outperformed over the other 3 formulas in patients with PACD. Subgroup analysis showed that the group with long AL has lower values of median absolute PE. PE was significantly positively correlated with AL and negatively correlated with relative lens position when calculated using BU II and Kane. Haigis, Kane, and BU II formula achieved a comparable outcome and outperformed over the other 3 formulas in patients with PACD.

Retrospective assessment of accuracy of nine intraocular lens power calculation formulae in eyes with axial myopia.

To compare the accuracy of nine conventional and newer-generation formulae in calculating intraocular lens power in eyes with axial myopia. Tertiary eye care center, Bengaluru, India. Retrospective cross-sectional, comparative study conducted in India. Patients undergoing uneventful phacoemulsification in eyes with axial length >26 mm were included. Preoperative biometry was done using Lenstar LS 900 (Haag-Streit AG, Switzerland). Single eye of patients undergoing bilateral implantation was randomly selected. Optimized lens constants were used to calculate the predicted postoperative refraction of each formula, which was then compared with the actual refractive outcomes to give the prediction errors, following which subgroup analysis was performed. The Kane formula, Barrett universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill Radial Basis Function (Hill RBF) 3.0, Olsen formula, along with Wang Koch-adjusted four formulae, that is, Sanders Retzlaff Kraff/Theoretical (SRK/T), Holladay 1, Haigis, and Hoffer Q formula, were compared for intraocular lens power calculations. One hundred and sixty-five eyes that fulfilled all the inclusion criteria were studied. Hill RBF 3.0 had the lowest mean and median absolute prediction errors (0.355 and 0.275, respectively) compared to all formulas. In subgroup analysis (26-28, >28-30, and >30 mm), significant difference was seen only in extremely long eyes (>30 mm). The Hill RBF 3.0 formula generated the maximum percentage of eyes with refractive errors within ±0.25, ±0.5, ±0.75, and ±1 D (46%, 76.2%, 89.9%, and 95.8%, respectively). This is the first study evaluating all the formulas exclusively in the myopic eyes. Hill RBF 3 was found to be superior in accuracy to all other formulas.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BackgroundPredicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. ObjectivesTo generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. MethodsTEE derived from “gold standard” methods from n = 1657 (n = 1019 females, age range 65–90 y), was used to generate PAL values. PAL ranged 1.28–2.05 for males and 1.26–2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. ResultsEquations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = −5680.17 − 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = −5290.72 − 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. ConclusionsThese new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

The LISA-PPV Formula: An Ensemble Artificial Intelligence-Based Thick Intraocular Lens Calculation Formula for Vitrectomized Eyes

PurposeTo investigate the relationship between effective lens position (ELP) and patient characteristics, and to further develop a new intraocular lens (IOL) calculation formula for cataract patients with previous pars plana vitrectomy (PPV). DesignCross-sectional study. MethodsA total of 2793 age-related cataract patients (group 1) and 915 post-PPV cataract patients (group 2) who underwent phacoemulsification with IOL implantation were included. The ELP of two groups was compared and the association between ELP and patient characteristics was further evaluated using standardized multivariate regression coefficients. An ensemble artificial intelligence-based ELP prediction model was developed using a training set of 810 vitrectomized eyes, and a thick-lens IOL formula (LISA-PPV) was constructed and compared with seven existing formulas on an external multi-center testing set of 105 eyes. ResultsCompared to eyes with age-related cataract, vitrectomized eyes showed a similar ELP distribution (P = 0.19), but different standardized coefficients of preoperative biometry for ELP. The standardized coefficients also varied with the type of vitreous tamponade, history of scleral buckling, and ciliary sulcus IOL implantation. The LISA-PPV formula showed the lowest mean and median absolute prediction error (MAE: 0.63 D; MedAE: 0.44 D), and the highest percentages of eyes within ± 0.5 D of prediction error (57.14%) in the testing dataset. ConclusionsThe ELP prediction required optimization specifically for vitrectomized eyes based on their biometric and surgical characteristics. The LISA-PPV formula is a useful and accurate tool for determining IOL power in cataract patients with previous PPV (available at http://ppv-iolcalculator.com/).

Predictive performance of population pharmacokinetic models of imatinib in chronic myeloid leukemia patients

Background and aimChronic myeloid leukemia is a myeloproliferative neoplasm associated with the specific chromosomal translocation known as the Philadelphia chromosome. Imatinib is a potent BCR-ABL tyrosine kinase inhibitor, which is approved as the first line therapy for CML patients. There are various population pharmacokinetic studies available in the literature for this population. However, their use in other populations outside of their cohort for the model development has not been evaluated. This study was aimed to perform the predictive performance of the published population pharmacokinetic models for imatinib in CML population and propose a dosing nomogram.MethodsA systematic review was conducted through PubMed, and WoS databases to identify PopPK models. Clinical data collected in adult CML patients treated with imatinib was used for evaluation of these models. Various prediction-based metrics were used for assessing the bias and precision of PopPK models using individual predictions.ResultsEight imatinib PopPK model were selected for evaluating the model performance. A total of 145 plasma imatinib samples were collected from 43 adult patients diagnosed with CML and treated with imatinib. The PopPK model reported by Menon et al. had better performance than all other PopPK models.ConclusionMenon et al. model was able to predict well for our clinical data where it had the relative mean prediction error percentage ≤ 20%, relative median absolute prediction error ≤ 30% and relative root mean square error close to zero. Based on this final model, we proposed a dosing nomogram for various weight groups, which could potentially help to maintain the trough concentrations in the therapeutic range.

Intraocular lens calculation using the ESCRS online calculator in pediatric eyes undergoing lens extraction.

To evaluate the ESCRS online calculator for intraocular lens (IOL) calculation in children undergoing lens extraction and primary IOL implantation. Department of Ophthalmology, Goethe-University Frankfurt, Frankfurt am Main, Germany. Retrospective, consecutive case series. Eyes that received phacoemulsification and IOL implantation (Acrysof SN60AT) due to congenital or juvenile cataract were included. We compared the mean prediction error (MPE), mean and median absolute prediction error (MAE, MedAE) of formulas provided by the recently introduced online calculator provided by the ESCRS with the SRK/T formula, as well as the number of eyes within ±0.5 diopters (D), ±1.0 D, ±2.0 D of target refraction. Postoperative spherical equivalent was measured by retinoscopy 4 to 12 weeks postoperatively. 60 eyes from 47 patients with a mean age of 6.5 ± 3.2 years met the inclusion criteria. Mean axial length was 22.27 ± 1.19 mm. Mean preoperative spherical equivalent (SE) was -0.25 ± 3.78 D, and mean postoperative SE was 0.69 ± 1.53 D. The MedAE was lowest in the SRK/T formula (0.56 D, ± 1.03) performed significantly better ( P = .037) than Hoffer QST and Kane, followed by BUII (0.64 D, ± 0.92), Pearl DGS (0.65 D, ± 0.94), EVO (0.69 D, ± 0.94), Hoffer QST (0.75 D, ± 0.99), and Kane (0.78 D, ± 0.99). All of those were significantly above zero ( P < .001). 41 eyes received an intraoperative optic capture (68%). When excluding eyes that did not receive intraoperative optic capture (n = 19; 32%), the MedAE was shown to be lower. Using modern IOL calculation formulas provided by the ESCRS calculator provides good refractive predictability and compares for most of the formulas with the results with SRK/T. In addition, the formulas seem to anticipate the postoperative refraction better for eyes that receive a posterior optic capture.

Interchangeability of bilateral anterior chamber depth in intraocular lens power calculation and its application in unilateral lens subluxation

Aims/Purpose: To assess the interchangeability of bilateral anterior chamber depth (ACD) in intraocular lens (IOL) power calculation, and to evaluate whether using the unaffected fellow eye's ACD can enhance refractive results in unilateral lens subluxation.Methods: Axial length (AL), keratometry (K) and ACD were obtained by IOLMaster 700. In cataract surgery candidates (Group 1), the virtual implantation of the same IOL model was used. The predictive results closest to the Plano of the Kane formula, using the right eye's biometry (AL, K and ACD), served as a benchmark. The results of the corresponding IOL power were further calculated by substituting the left eye's ACD for the right eye's one and compared with the benchmark. Eyes with unilateral lens subluxation (Group 2) underwent in‐the‐bag IOL placement after the capsular hook(s) implantation, with refraction conducted at least 1 month post‐operatively. Prediction errors (PE) of the Kane formula with or without the unaffected fellow eye's ACD were compared.Results: 392 eyes of 196 patients were analysed, including 173 candidates and 23 patients with unilateral subluxated lenses. In Group 1, there was no significant difference in bilateral ACD (p = 0.30). The predictive refraction using the right eye's ACD was −0.16 ± 0.10 diopter (D), and that obtained by the left eye's one was −0.16 ± 0.11 D (p = 0.64), with an absolute difference of 0.03 ± 0.03 D. In Group 2, the ACD was 2.89 ± 0.86 mm of the affected eye and 3.14 ± 0.37 mm of the fellow eye (p = 0.17). The median absolute PE, mean absolute PE and the percentage of PEs within ±0.50 D using the affected eye's ACD were 0.50 D, 0.59 D and 52.17%. Using the unaffected eye's ACD improved the outcomes to 0.45D, 0.52 D and 60.87%.Conclusions: Bilateral ACD values are interchangeable in routine IOL power calculation, and using the unaffected fellow eye's ACD can improve its accuracy in cases of unilateral lens subluxation undergoing in‐the‐bag IOL implantation.

Refractive Predictability between Standard and Total Keratometry during the Femtosecond Laser-Assisted Cataract Surgery with Monofocal Intraocular Lens with Enhanced Intermediate Function.

We aimed to compare the accuracy of the intraocular lens (IOL) calculation formula using the standard keratometry (K) and total K (TK) during the femtosecond laser-assisted cataract surgery (FLACS) with a monofocal IOL with enhanced intermediate function using currently used formulas. A retrospective review of 125 eyes from 125 patients who had undergone FLACS with implantation of monofocal IOL with enhanced intermediate function was conducted. The predicted refractive power was calculated using an optical biometer (IOLmaster 700) according to the K and TK in the Barrett Universal II, SRK/T, Haigis, and Holladay 2 formulas. Absolute prediction error (APE) obtained from the actual postoperative refractive outcomes and the refractive error predicted in each formula was compared one month after surgery. Mean APE ranged between 0.29 and 0.39 diopters (D) regardless of the calculation formula and the method of measuring corneal curvature. Significant differences were observed in the APE from the four formulas and the two keratometric measurements (p = 0.014). In a total of 125 eyes from 125 patients, the mean APE was lowest with the Barrett Universal II formula. Across all formulas, both the mean APE and the median APE tended to be lower for K than for TK, although there was no significant difference. Approximately 70% to 80% of the patients were included within 0.5 D of the refractive error across all formulas. The percentage of eyes within 0.5 D of APE outcomes was not statistically different between the K and TK data when using each formula. Keratometric measurements considering the poster corneal curvature did not show any additional advantages when implanting the monofocal IOL with enhanced intermediate function during the FLACS.

Prospective evaluation of the ESCRS online calculator for calculation of a multifocal intraocular lens.

To evaluate a recently introduced ESCRS online calculator for intraocular lens (IOL) calculation of a multifocal IOL in refractive lens exchange and cataract surgery in a prospective setting. Department of Ophthalmology, Goethe University Frankfurt, Germany. Prospective, consecutive case series. We included eyes that received lens extraction and multifocal IOL implantation. We compared the mean prediction error (MPE), mean and median absolute prediction error (MAE, MedAE) of the online calculator provided by the European Society of Cataract and Refractive Surgeons (ESCRS), and number of eyes within ±0.5, ±1.0, ±2.0 diopters (D) of target refraction. Additionally, the SRK/T formula was included for comparison as well. Postoperative spherical equivalent was measured at 3 months. Only one eye per patient was included. Eighty-eight eyes from 88 patients with mean age of 62 years ± 9.5 were included. The MedAE was low for all formulas and ranged from 0.26D (Kane), Hill-RBF (0.27D), Hoffer QST (0.27D), Barrett (0.28D), EVO (0.29D), Cooke K6 (0.27D), 0.30D (Pearl DGS) to 0.31D (SRK/T). No statistically significant difference was found (p = 0.627). Considering the number of eyes within ±0.5D of the calculated refraction the best performing was again the Hill-RBF (84%, 74 eyes), again followed by Kane (71, 81%), EVO, Pearl DGS, Hoffer QST, Barrett (each 80%, 70 eyes),Cooke K6 (78%, 69 eyes), and SRK/T (74%). Again, no statistically significant difference was found (p = 0.39). Using recently introduced ESCRS online IOL calculator in multifocal intraocular lenses leads to a high number of eyes reaching target refraction and low prediction errors. All formulas performed similar well. However, Hill-RBF showed the highest number of eyes within ±0.5D, but no significance was found regarding this.

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population.

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Accuracy of the PEARL-DGS Formula for Intraocular Lens Power Calculation in Post–Myopic Laser Refractive Corneal Surgery Eyes

PurposeTo investigate the accuracy of the PEARL-DGS formula for intraocular lens (IOL) power calculation in post-myopic laser refractive corneal surgery eyes. DesignRetrospective case series MethodsA total of 139 eyes of 139 patients (mean axial length: 27.4 ± 2.1 mm), who had prior myopic laser refractive corneal surgery and subsequent cataract surgery using Tecnis ZCB00 from March 2018 to February 2023 were included. Refractive outcomes of five formulas (Barrett True K, Haigis-L, Hoffer-QST, PEARL-DGS, and Shammas-PL) were evaluated. Prediction error was defined as the difference between the measured and predicted postoperative refractive spherical equivalent using the IOL power actually implanted. Mean prediction error (MPE), median absolute prediction error (MedAE), mean absolute prediction error were calculated. ResultsWithout constant optimization, the PEARL-DGS resulted in a MPE of +0.05 ± 0.65 D, while the other formulas resulted in myopic shifts. The MedAEs of the formulas were 0.39, 0.53, 0.65, 0.85, and 1.11 D for the PEARL-DGS, Hoffer-QST, Barrett True K, Shammas-PL, and Haigis-L, respectively, in order of magnitude (P < 0.05). With constant optimization, there were no statistically significant differences in the MedAEs among the five formulas (P = 0.388). ConclusionsIn comparison to other IOL formulas, the PEARL-DGS resulted in better refractive outcomes after cataract surgery in post-myopic laser refractive corneal surgery eyes without constant optimization. We suggest that PEARL-DGS be considered as the first choice for IOL power calculation in these eyes when the clinicians do not have their optimized constants.

Prophylactic norepinephrine combined with 6% hydroxyethyl starch (130/0.4) co-load infusion for preventing postspinal anesthesia hypotension during cesarean section: a randomized, controlled, dose-finding trial.

Colloid and/or co-load may be more effective than crystalloid for preventing postspinal anesthesia hypotension. We tested five different prophylactic norepinephrine dosages combined with colloid co-load infusion in patients receiving cesarean section and spinal anesthesia. Patients were randomly allocated to receive different prophylactic norepinephrine dosages (0 [NE 0 group], 0.025 [NE 25 group], 0.05 [NE 50 group], 0.075 [NE 75 group], or 0.1 [NE 100 group] µg/kg/min) combined with 500mL 6% hydroxyethyl starch (130/0.4) immediately following spinal anesthesia (n = 35 per group). The primary endpoint was the incidence of postspinal anesthesia hypotension (systolic blood pressure [SBP] < 80% of baseline). Secondary endpoints included severe hypotension, bradycardia, nausea or vomiting, hypertension, SBP stability control versus baseline, the 50% (effective dose, ED50) and 90% (ED90) dose effective for preventing postspinal anesthesia hypotension, Apgar scores, and umbilical cord blood gases. The incidence of postspinal anesthesia hypotension was 48.6%, 31.3%, 17.1%, 14.3%, and 5.7% in the respective groups. As the prophylactic norepinephrine dosage increased, the incidence of postspinal anesthesia hypotension declined (p < 0.001), and SBP remained stable relative to baseline (median performance error [MDPE], p < 0.001; median absolute performance error [MDAPE], p = 0.001). The ED50 and ED90 values were -0.006 (95% CI -0.046-0.013) and 0.081 (95% CI 0.063-0.119) µg/kg/min. Other endpoints were comparable across the groups. An initial prophylactic norepinephrine dosage of 0.05µg/kg/min combined with 500mL 6% hydroxyethyl starch (130/0.4) co-load infusion was optimal for preventing postspinal anesthesia hypotension during cesarean section. NCT05133817, registration date: 12 Nov, 2021.

Modeling the protein binding non-linearity in population pharmacokinetic model of valproic acid in children with epilepsy: a systematic evaluation study.

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies. Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability. Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F20, 55.69%) and 30% (F30, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models. Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.