Integrated triboelectric nanogenerator and radiative cooler for all-weather transparent glass surfaces.

Sustainable energies from weather are the most ubiquitous and non-depleted resources. However, existing devices exploiting weather-dependent energies are sensitive to weather conditions and geographical locations, making their universal applicability challenging. Herein, we propose an all-weather sustainable glass surface integrating a triboelectric nanogenerator and radiative cooler, which serves as a sustainable device, harvesting energy from raindrops and saving energy on sunny days. By systematically designing transparent, high-performance triboelectric layers, functioning as thermal emitters simultaneously, particularly compatible with radiative cooling components optimized with an evolutionary algorithm, our proposed device achieves optimal performance for all-weather-dependent energies. We generate 248.28 Wm-2 from a single droplet with an energy conversion ratio of 2.5%. Moreover, the inner temperature is cooled down by a maximum of 24.1 °C compared to pristine glass. Notably, as the proposed device is realized to provide high transparency up to 80% in the visible range, we are confident that our proposed device can be applied to versatile applications.

Quantitative Analysis of the Effect of Neuromuscular Blockade on Motor-Evoked Potentials in Patients Undergoing Brain Tumor Removal Surgery: A Prospective, Single-Arm, Open-Label Observational Study.

Background: We aimed to elucidate the quantitative relationship between the neuromuscular blockade depth and intraoperative motor-evoked potential amplitudes. Methods: This prospective, single-arm, open-label, observational study was conducted at a single university hospital in Seoul, Korea, and included 100 adult patients aged ≥19 years undergoing brain tumor removal surgery under general anesthesia. We measured the neuromuscular blockade degree and motor-evoked potential amplitude in the deltoid, abductor pollicis brevis, tibialis anterior, and abductor hallucis muscles until dural opening. Results: The pharmacokinetic-pharmacodynamic model revealed the exposure-response relationship between the rocuronium effect-site concentration and motor-evoked potential amplitudes. The mean motor-evoked potential amplitudes decreased proportionally with increasing neuromuscular blockade depth. As the mean amplitude increased, the coefficient of variation decreased bi-exponentially. The critical ratio of the first evoked response to the train-of-four stimulation (T1)/control response (Tc) thresholds beyond which the coefficient of variation exhibited minimal change were found to be 0.63, 0.65, 0.68, and 0.63 for the deltoid, abductor pollicis brevis, tibialis anterior, and abductor hallucis muscles, respectively. Conclusions: Our results reveal that the motor-evoked potential amplitude exhibits deterioration proportional to the degree of neuromuscular blockade. In light of the observed bi-exponential decline of the coefficient of variation with the motor-evoked potential amplitude, we recommend maintaining a T1/Tc ratio higher than 0.6 for partial neuromuscular blockade.

Pharmacokinetic Modeling of Bepotastine for Determination of Optimal Dosage Regimen in Pediatric Patients with Allergic Rhinitis or Urticaria.

Bepotastine, a second-generation antihistamine for allergic rhinitis and urticaria, is widely used in all age groups but lacks appropriate dosing guidelines for pediatric patients, leading to off-label prescriptions. We conducted this study to propose an optimal dosing regimen for pediatric patients based on population pharmacokinetic (popPK) and physiologically based pharmacokinetic (PBPK) models using data from two previous trials. A popPK model was built using NONMEM software. A one-compartment model with first-order absorption and absorption lag time described our data well, with body weight incorporated as the only covariate. A PBPK model was developed using PK-Sim software version 10, and the model well predicted the drug concentrations obtained from pediatric patients. Furthermore, the final PBPK model showed good concordance with the known properties of bepotastine. Appropriate pediatric doses for different weight and age groups were proposed based on the simulations. Discrepancies in recommended doses from the two models were likely due to the incorporation of age-dependent physiological factors in the PBPK model. In conclusion, our study is the first to suggest an optimal oral dosing regimen of bepotastine in pediatric patients using both approaches. This is expected to foster safer and more productive use of the drug.

Smart building block with colored radiative cooling devices and quantum dot light emitting diodes.

In this study, we design a smart building block with quantum-dot light-emitting diode (QLED) and colored radiative cooling devices. A smart light-emitting building block is fabricated using a bottom-inverted QLED that emits green light, an insulating layer, and a top radiative cooling structure that emits mid-infrared light. The heat generated during QLED operation is measured and analyzed to investigate the correlation between heat and QLED degradation. The top cooling part is designed to have no impact on the QLED's performance and utilizes Ag-polydimethylsiloxane as a visible-light reflector and mid-infrared absorber/emitter. For the colored cooling part, white radiative cooling paint is used instead of Ag-polydimethylsiloxane to improve cooling performance, and red and yellow paints are employed to realize vivid red and yellow colors, respectively. We demonstrate a smart imitation house system with a smart light-emitting building block as the roof and analyze the cooling of the heat generated during QLED operation. A maximum cooling effect of up to 9.6 °C is observed compared to the imitation house system without the smart light-emitting building block, effectively dissipating heat generated during QLED operation. The smart light-emitting building block presented in this study opens new avenues in the fields of lighting and cooling systems.

Population pharmacokinetic-pharmacodynamic modeling of clopidogrel for dose regimen optimization based on CYP2C19 phenotypes: A proof of concept study.

Clopidogrel is an antiplatelet drug used to reduce the risk of acute coronary syndrome and stroke. It is converted by CYP2C19 to its active metabolite; therefore, poor metabolizers (PMs) of CYP2C19 exhibit diminished antiplatelet effects. Herein, we conducted a proof-of-concept study for using population pharmacokinetic-pharmacodynamic (PK-PD) modeling to recommend a personalized clopidogrel dosing regimen for individuals with varying CYP2C19 phenotypes and baseline P2Y12 reaction unit (PRU) levels. Data from a prospective phase I clinical trial involving 36 healthy male participants were used to develop the population PK-PD model predicting the concentrations of clopidogrel, clopidogrel H4, and clopidogrel carboxylic acid, and linking clopidogrel H4 concentrations to changes in PRU levels. A two-compartment model effectively described the PKs of both clopidogrel and clopidogrel carboxylic acid, and a one-compartment model of those of clopidogrel H4. The CYP2C19 phenotype was identified as a significant covariate influencing the metabolic conversion of the parent drug to its metabolites. A PD submodel of clopidogrel H4 that stimulated the fractional turnover rate of PRU levels showed the best performance. Monte Carlo simulations suggested that PMs require three to four times higher doses than extensive metabolizers to reach the target PRU level. Individuals within the top 20th percentile of baseline PRU levels were shown to require 2.5-3 times higher doses than those in the bottom 20th percentile. We successfully developed a population PK-PD model for clopidogrel considering the impact of CYP2C19 phenotypes and baseline PRU levels. Further studies are necessary to confirm actual dosing recommendations for clopidogrel.

Vivid Colored Cooling Structure Managing Full Solar Spectrum via Near-Infrared Reflection and Photoluminescence.

Colored radiative cooling (CRC) offers an attractive alternative for surface and space cooling, while preserving the aesthetics of an object. However, there has been no study on the CRC using phosphors in regard to vivid coloration, sophisticated performance investigation, retention of properties, functionality, and structural flexibility all at once. Thus, to manage the entire solar spectrum, a colored cooling structure comprising a near-infrared (NIR)-reflective bottom layer and a top colored layer with a phosphor-embedded polymer matrix is proposed. The structure is paintable, vividly colored, hydrophobic, and ultraviolet (UV) and water resistant. In the daytime outdoor measurement, the structure with red, orange, and yellow colors exhibited lower temperature than a control group using commercial white paint by 4.7 °C, 7.2 °C, and 7.4 °C, respectively. After precise theoretical and experimental time-tracing temperature validation, the CRC performance enhancement from NIR reflection and photoluminescence effects was thoroughly analyzed, and a temperature reduction of up to 16.1 °C was achieved for the orange-colored structure. Furthermore, experiments of hydrophobicity infusion and exposure to UV and deionized water verified the durability of the colored cooling structure. In addition, flexible-film-type colored cooling structures were demonstrated using different bottom reflective layers, such as a silver thin film and porous aluminum oxide particle-embedded poly(vinylidene fluoride-co-hexafluoropropylene), suggesting the potential applicability of these colored cooling structures for vivid-colored, functional, and durable CRC.

Model-informed precision dosing in vancomycin treatment.

Introduction: While vancomycin remains a widely prescribed antibiotic, it can cause ototoxicity and nephrotoxicity, both of which are concentration-associated. Overtreatment can occur when the treatment lasts for an unnecessarily long time. Using a model-informed precision dosing scheme, this study aims to develop a population pharmacokinetic (PK) and pharmacodynamic (PD) model for vancomycin to determine the optimal dosage regimen and treatment duration in order to avoid drug-induced toxicity. Methods: The data were obtained from electronic medical records of 542 patients, including 40 children, and were analyzed using NONMEM software. For PK, vancomycin concentrations were described with a two-compartment model incorporating allometry scaling. Results and discussion: This revealed that systemic clearance decreased with creatinine and blood urea nitrogen levels, history of diabetes and renal diseases, and further decreased in women. On the other hand, the central volume of distribution increased with age. For PD, C-reactive protein (CRP) plasma concentrations were described by transit compartments and were found to decrease with the presence of pneumonia. Simulations demonstrated that, given the model informed optimal doses, peak and trough concentrations as well as the area under the concentration-time curve remained within the therapeutic range, even at doses smaller than routine doses, for most patients. Additionally, CRP levels decreased more rapidly with the higher dose starting from 10 days after treatment initiation. The developed R Shiny application efficiently visualized the time courses of vancomycin and CRP concentrations, indicating its applicability in designing optimal treatment schemes simply based on visual inspection.

Development of a population pharmacokinetic model and optimal dosing regimen of leflunomide in Korean population.

PURPOSE: Leflunomide is an immunosuppressive drug indicated for the treatment of rheumatoid arthritis (RA). While the pharmacokinetics (PK) of its active metabolite A771726 reportedly show large interindividual variability, no efficient dose individualization strategy is currently available. The goal of this work was to develop a population PK model for A771726 and propose an optimal individualized dosing strategy. METHODS: A771726 plasma concentration data were collected from 50 healthy male volunteers participating in two leflunomide PK studies given a single oral dose of 40 mg. Concentrations were elevated in low body weight (WT) subjects and showed multiple peaks. Thus, A771726 PK modeling was conducted incorporating allometry scaling and enterohepatic circulation (EHC). For dose optimization, simulating a set of 1000 virtual subjects from the developed model and dividing the subjects into 5 groups with WT of 50, 60, 70, 80, 90 kg, respectively, the optimal dose was explored that achieves the drug concentration most similar to the target, which was defined as the concentration for the 70 kg subject treated with the current standard dosage regimen (the loading dose of 100 mg QD for 3 days, followed by the maintenance dose of 20 mg QD). RESULTS: The data were best described by a two compartment model with first order absorption incorporating EHC with the bile released into the intestine. None of the covariates tested was found to be significant other than WT used in allometry. Simulation showed that the optimal loading dose increased by 15 mg for every 10 kg increment in WT while the optimal maintenance dose was 15 and 25 mg for 50 and 90 kg groups, respectively, and the same (= 20 mg) for the others. Large concentration differences from the target observed in low and high WT groups disappeared when optimal doses were given. CONCLUSIONS: This work demonstrates the importance of a population PK model-based dose optimization approach in maintaining drug therapeutic concentrations in leflunomide treatment.

Phage-host-immune system dynamics in bacteriophage therapy: basic principles and mathematical models.

Phage therapy is progressively being recognized as a viable alternative to conventional antibiotic treatments, particularly in the context of multi-drug resistant bacterial challenges. However, the intricacies of the pharmacokinetics and pharmacodynamics (PKPD) pertaining to phages remain inadequately elucidated. A salient characteristic of phage PKPD is the inherent ability of phages to undergo replication. In this review, I proffer mathematical models that delineate the intricate dynamics encompassing the phage, the host organism, and the immune system. Fundamental tenets associated with proliferative and inundation thresholds are explored, and distinctions between active and passive therapies are accentuated. Furthermore, I present models that aim to illuminate the multifaceted interactions amongst diverse phage strains and bacterial subpopulations, each possessing distinct sensitivities to phages. The synergistic relationship between phages and the immune system is critically examined, demonstrating how the host's immunological function can influence the requisite phage dose for an optimal therapeutic outcome. A profound understanding of the presented modeling methodologies is paramount for researchers in the realms of clinical pharmacology and PKPD modeling interested in phage therapy. Such insights facilitate a more nuanced interpretation of dose-response relationships, enable the selection of potent phages, and aid in the optimization of phage cocktails.

Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models.

The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospital between January 2010 and September 2020 were included as data samples. Preoperative conditions of recipients and donors, intraoperative care, postoperative serial laboratory parameters, and events observed within seven days of surgery were collected as features. A least absolute shrinkage and selection operator (LASSO) -based method was used for feature selection to overcome the high dimensionality and collinearity of variables. Among 146 features, four variables were selected as the resultant features, namely, preoperative hepatic encephalopathy, sodium level at the end of surgery, hepatic artery thrombosis, and total bilirubin level on postoperative day 7. These features were selected from different times and represent distinct clinical aspects. The model with logistic regression demonstrated the best prediction performance among various machine learning methods tested (area under the receiver operating characteristic curve (AUROC) = 0.898 and area under the precision-recall curve (AUPR) = 0.882). The risk scoring system developed based on the logistic regression model showed an AUROC of 0.910 and an AUPR of 0.830. Together, the prediction of graft failure in pediatric liver transplantation using the proposed machine learning model exhibited superior discrimination power and, therefore, can provide valuable information to clinicians for their decision making during the postoperative management of the patients.

Development of Machine Learning Models Predicting Estimated Blood Loss during Liver Transplant Surgery.

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).

Designing a self-classifying smart device with sensor, display, and radiative cooling functions via spectrum-selective response.

This paper presents a self-classifying smart device that intelligently differentiates and operates three functions: electroluminescence display, ultraviolet light sensor, and thermal management via radiative cooling. The optical and electrical properties of the materials and structures are designed to achieve a spectrum-selective response, which enables the integration of the aforementioned functions into one device without any noise or interference. Spectrum-selective materials that absorb, emit, and radiate light with ultraviolet to mid-infrared wavelengths and device structures designed to prevent interference are achieved by using thin metal films, dielectric layers, and nanocrystals. The designed self-classifying smart device exhibits bright blue light emission upon current supply (display), green light emission upon exposure to UV light (sensor), and radiative cooling (thermal management). Furthermore, a smart device and house system with a display, UV light sensor, and radiative cooling performance was demonstrated. The findings of this study open new avenues for device integration in next-generation wearable device fabrication.

A risk scoring system integrating postoperative factors for predicting early mortality after major non-cardiac surgery.

We aimed to develop a risk scoring system for 1-week and 1-month mortality after major non-cardiac surgery, and assess the impact of postoperative factors on 1-week and 1-month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non-cardiac surgery and collected pre-, intra-, and postoperative features. We derived two patient cohorts to predict 1-week and 1-month mortality and randomly split each of them into training and test cohorts at a ratio of 8:2. All the modeling steps were carried out solely based on the training cohorts, whereas the test cohorts were reserved for the evaluation of predictive performance. Incorporation of postoperative information demonstrated no significant benefit in predicting 1-week mortality but led to substantial improvement in predicting 1-month mortality. Risk scores predicting 1-week and 1-month mortality were associated with area under receiver operating characteristic curves of 84.58% and 90.66%, respectively. Brain surgery, amount of intraoperative red blood cell transfusion, preoperative platelet count, preoperative serum albumin, and American Society of Anesthesiologists physical status were included in the risk score predicting 1-week mortality. Postoperative day (POD) 5 (neutrophil count × mean platelet volume) to (lymphocyte count × platelet count) ratio, preoperative and POD 5 serum albumin, and occurrence of acute kidney injury were included in the risk score predicting 1-month mortality. Our scoring system advocates the importance of postoperative complete blood count differential and serum albumin to better predict mortality beyond the first week post-surgery.

Pharmacodynamic analysis of intravenous bolus remimazolam for loss of consciousness in patients undergoing general anaesthesia: a randomised, prospective, double-blind study.

BACKGROUND: Remimazolam is a new rapid offset benzodiazepine used for procedural sedation and general anaesthesia. This study evaluated the efficacy and safety of i.v. bolus remimazolam during induction of anaesthesia. METHODS: A total of 120 patients undergoing general anaesthesia were randomly allocated into six dose groups (n=20) of i.v. bolus remimazolam (0.02-0.27 mg kg-1). Loss of consciousness, respiratory depression, patient state index (PSI), and haemodynamic variables were evaluated during anaesthetic induction. Parametric time-to-event models were used to identify the 50% effective dose (ED50)/95% effective dose (ED95) associated with loss of consciousness and respiratory depression. Non-linear mixed-effect models analysed the PSI and haemodynamic changes after i.v. bolus remimazolam. RESULTS: Loss of consciousness and respiratory depression onset showed steep dose-responses with ED50/ED95 of 0.11/0.19 and 0.14/0.27 mg kg-1 and Hill coefficients of 5.3 and 4.6, respectively. Older age was significantly associated with lower ED50/ED95 for both endpoints. ED50/ED95 and the Hill coefficient of PSI decline were 0.12/0.68 mg kg-1 and 1.7, respectively. We propose optimal doses of 0.25-0.33, 0.19-0.25, and 0.14-0.19 mg kg-1 in patients aged <40, 60-80, and >80 yr, respectively, based on the ED95 estimates for the corresponding age groups. The maximum percentage reduction of MAP was 27.8% and the ED50/ED95 were 0.14/2.60 mg kg-1. The effect of remimazolam on heart rate was insignificant. CONCLUSIONS: The ED50/ED95s of i.v. bolus remimazolam were successfully estimated from the time to loss of consciousness and respiratory depression. No serious adverse events occurred within the range of tested doses. CLINICAL TRIAL REGISTRATION: NCT04901871.

Effects of Remimazolam vs. Sevoflurane Anesthesia on Intraoperative Hemodynamics in Patients with Gastric Cancer Undergoing Robotic Gastrectomy: A Propensity Score-Matched Analysis.

Remimazolam has been suggested to improve the maintenance of hemodynamic stability when compared with other agents used for general anesthesia. This study aimed to compare the effects of remimazolam and sevoflurane anesthesia on hemodynamic stability in patients undergoing robotic gastrectomy. We retrospectively reviewed the electronic medical records of 199 patients who underwent robotic gastrectomy with sevoflurane (n = 135) or remimazolam (n = 64) anesthesia from January to November 2021. Propensity scores were used for 1:1 matching between the groups. The primary outcome was the difference in use of intraoperative vasopressors between groups. Secondary outcomes included differences in incidence and dose of vasopressors, as well as intraoperative hemodynamic variables, between groups. Remimazolam anesthesia was associated with a significantly less frequent use of ephedrine (odds ratio (OR): 0.13; 95% confidence interval (CI): 0.05−0.38, p < 0.001), phenylephrine (OR: 0.12; 95% CI: 0.04−0.40, p < 0.001), and any vasopressor (OR: 0.06; 95% CI: 0.02−0.25, p < 0.001) compared with sevoflurane anesthesia. Remimazolam anesthesia enables better maintenance of hemodynamic stability than sevoflurane anesthesia. Thus, remimazolam anesthesia may be beneficial for patients who are expected to experience hypotension due to the combined effects of CO2 pneumoperitoneum and the head-up position utilized during robotic gastrectomy.

A Pharmacometric Model to Predict Chemotherapy-Induced Myelosuppression and Associated Risk Factors in Non-Small Cell Lung Cancer.

Chemotherapy often induces severe neutropenia due to the myelosuppressive effect. While predictive pharmacokinetic (PK)/pharmacodynamic (PD) models of absolute neutrophil count (ANC) after anticancer drug administrations have been developed, their deployments to routine clinics have been limited due to the unavailability of PK data and sparseness of PD (or ANC) data. Here, we sought to develop a model describing temporal changes of ANC in non-small cell lung cancer patients receiving (i) combined chemotherapy of paclitaxel and cisplatin and (ii) granulocyte colony stimulating factor (G-CSF) treatment when needed, under such limited circumstances. Maturation of myelocytes into blood neutrophils was described by transit compartments with negative feedback. The K-PD model was employed for drug effects with drug concentration unavailable and the constant model for G-CSF effects. The fitted model exhibited reasonable goodness of fit and parameter estimates. Covariate analyses revealed that ANC decreased in those without diabetes mellitus and female patients. Using the final model obtained, an R Shiny web-based application was developed, which can visualize predicted ANC profiles and associated risk of severe neutropenia for a new patient. Our model and application can be used as a supportive tool to identify patients at the risk of grade 4 neutropenia early and suggest dose reduction.

High-throughput functional evaluation of human cancer-associated mutations using base editors.

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

A Simple Risk Scoring System for Predicting the Occurrence of Aspiration Pneumonia After Gastric Endoscopic Submucosal Dissection.

BACKGROUND: Aspiration pneumonia after endoscopic submucosal dissection (ESD) is rare, but can be fatal. We aimed to investigate risk factors and develop a simple risk scoring system for aspiration pneumonia. METHODS: We retrospectively reviewed medical records of 7833 patients who underwent gastric ESD for gastric neoplasm under anesthesiologist-directed sedation. Candidate risk factors were screened and assessed for significance using a least absolute shrinkage and selection operator (LASSO)-based method. Top significant factors were incorporated into a multivariable logistic regression model, whose prediction performance was compared with those of other machine learning models. The final risk scoring system was created based on the estimated odds ratios of the logistic regression model. RESULTS: The incidence of aspiration pneumonia was 1.5%. The logistic regression model showed comparable performance to the best predictive model, extreme gradient boost (area under receiver operating characteristic curve [AUROC], 0.731 vs 0.740). The estimated odds ratios were subsequently used for the development of the clinical scoring system. The final scoring system exhibited an AUROC of 0.730 in the test dataset with risk factors: age (≥70 years, 4 points), male sex (8 points), body mass index (≥27 kg/m2, 4 points), procedure time (≥80 minutes, 5 points), lesion in the lower third of the stomach (5 points), tumor size (≥10 mm, 3 points), recovery time (≥35 minutes, 4 points), and desaturation during ESD (9 points). For patients with total scores ranging between 0 and 33 points, aspiration pneumonia probabilities spanned between 0.1% and 17.9%. External validation using an additional cohort of 827 patients yielded AUROCs of 0.698 for the logistic regression model and 0.680 for the scoring system. CONCLUSIONS: Our simple risk scoring system has 8 predictors incorporating patient-, procedure-, and sedation-related factors. This system may help clinicians to stratify patients at risk of aspiration pneumonia after ESD.

Predictive models for chronic kidney disease after radical or partial nephrectomy in renal cell cancer using early postoperative serum creatinine levels.

BACKGROUND: Several predictive factors for chronic kidney disease (CKD) following radical nephrectomy (RN) or partial nephrectomy (PN) have been identified. However, early postoperative laboratory values were infrequently considered as potential predictors. Therefore, this study aimed to develop predictive models for CKD 1 year after RN or PN using early postoperative laboratory values, including serum creatinine (SCr) levels, in addition to preoperative and intraoperative factors. Moreover, the optimal SCr sampling time point for the best prediction of CKD was determined. METHODS: Data were retrospectively collected from patients with renal cell cancer who underwent laparoscopic or robotic RN (n = 557) or PN (n = 999). Preoperative, intraoperative, and postoperative factors, including laboratory values, were incorporated during model development. We developed 8 final models using information collected at different time points (preoperative, postoperative day [POD] 0 to 5, and postoperative 1 month). Lastly, we combined all possible subsets of the developed models to generate 120 meta-models. Furthermore, we built a web application to facilitate the implementation of the model. RESULTS: The magnitude of postoperative elevation of SCr and history of CKD were the most important predictors for CKD at 1 year, followed by RN (compared to PN) and older age. Among the final models, the model using features of POD 4 showed the best performance for correctly predicting the stages of CKD at 1 year compared to other models (accuracy: 79% of POD 4 model versus 75% of POD 0 model, 76% of POD 1 model, 77% of POD 2 model, 78% of POD 3 model, 76% of POD 5 model, and 73% in postoperative 1 month model). Therefore, POD 4 may be the optimal sampling time point for postoperative SCr. A web application is hosted at https://dongy.shinyapps.io/aki_ckd . CONCLUSIONS: Our predictive model, which incorporated postoperative laboratory values, especially SCr levels, in addition to preoperative and intraoperative factors, effectively predicted the occurrence of CKD 1 year after RN or PN and may be helpful for comprehensive management planning.