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1.
Braz. j. biol ; 84: e259259, 2024. tab, graf
Artigo em Inglês | LILACS, VETINDEX | ID: biblio-1364517

RESUMO

Rice is a widely consumed staple food for a large part of the world's human population. Approximately 90% of the world's rice is grown in Asian continent and constitutes a staple food for 2.7 billion people worldwide. Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is one of the devastating diseases of rice. A field experiment was conducted during the year 2016 and 2017 to investigate the influence of different meteorological parameters on BLB development as well as the computation of a predictive model to forecast the disease well ahead of its appearance in the field. The seasonal dataset of disease incidence and environmental factors was used to assess five rice varieties/ cultivars (Basmati-2000, KSK-434, KSK-133, Super Basmati, and IRRI-9). The accumulated effect of two year environmental data; maximum and minimum temperature, relative humidity, wind speed, and rainfall, was studied and correlated with disease incidence. Average temperature (maximum & minimum) showed a negative significant correlation with BLB disease and all other variables; relative humidity, rainfall, and wind speed had a positive correlation with BLB disease development on individual varieties. Stepwise regression analysis was performed to indicate potentially useful predictor variables and to rule out incompetent parameters. Environmental data from the growing seasons of July to October 2016 and 2017 revealed that, with the exception of the lowest temperature, all environmental factors contributed to disease development throughout the cropping season. A disease prediction multiple regression model was developed based on two-year data (Y = 214.3-3.691 Max T-0.508 Min T + 0.767 RH + 2.521 RF + 5.740 WS), which explained 95% variability. This disease prediction model will not only help farmers in early detection and timely management of bacterial leaf blight disease of rice but may also help reduce input costs and improve product quality and quantity. The model will be both farmer and environmentally friendly.


O arroz é um alimento básico amplamente consumido por grande parte da população humana mundial. Aproximadamente 90% do arroz do mundo é cultivado no continente asiático e constitui um alimento básico para 2,7 bilhões de pessoas em todo o mundo. O crestamento bacteriano das folhas (BLB) causado por Xanthomonas oryzae pv. oryzae é uma das doenças devastadoras do arroz. Um experimento de campo foi realizado durante os anos de 2016 e 2017 para investigar a influência de diferentes parâmetros meteorológicos no desenvolvimento do BLB, bem como o cálculo de um modelo preditivo para prever a doença bem antes de seu aparecimento em campo. O conjunto de dados sazonais de incidência de doenças e fatores ambientais foi usado para avaliar cinco variedades/cultivares de arroz (Basmati-2000, KSK-434, KSK-133, Super Basmati e IRRI-9). O efeito acumulado de dados ambientais de dois anos; temperatura máxima e mínima, umidade relativa do ar, velocidade do vento e precipitação pluviométrica foram estudados e correlacionados com a incidência da doença. A temperatura média (máxima e mínima) apresentou correlação significativa negativa com a doença BLB e todas as outras variáveis; umidade relativa, precipitação e velocidade do vento tiveram uma correlação positiva com o desenvolvimento da doença BLB em variedades individuais. A análise de regressão stepwise foi realizada para indicar variáveis preditoras potencialmente úteis e para descartar parâmetros incompetentes. Os dados ambientais das safras de julho a outubro de 2016 e 2017 revelaram que, com exceção da temperatura mais baixa, todos os fatores ambientais contribuíram para o desenvolvimento da doença ao longo da safra. Um modelo de regressão múltipla de previsão de doença foi desenvolvido com base em dados de dois anos (Y = 214,3-3,691 Max T-0,508 Min T + 0,767 RH + 2,521 RF + 5,740 WS), que explicou 95% de variabilidade. Este modelo de previsão de doenças não só ajudará os agricultores na detecção precoce e gestão atempada da doença bacteriana das folhas do arroz, mas também pode ajudar a reduzir os custos de insumos e melhorar a qualidade e a quantidade do produto. O modelo será agricultor e ambientalmente amigável.


Assuntos
Oryza , Temperatura , Pragas da Agricultura , Umidade
2.
Adv Kidney Dis Health ; 30(1): 33-39, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36723279

RESUMO

A case study explores patterns of kidney function decline using unsupervised learning methods first and then associating patterns with clinical outcomes using supervised learning methods. Predicting short-term risk of hospitalization and death prior to renal dialysis initiation may help target high-risk patients for more aggressive management. This study combined clinical data from patients presenting for renal dialysis at Fresenius Medical Care with laboratory data from Quest Diagnostics to identify disease trajectory patterns associated with the 90-day risk of hospitalization and death after beginning renal dialysis. Patients were clustered into 4 groups with varying rates of estimated glomerular filtration rate (eGFR) decline during the 2-year period prior to dialysis. Overall rates of hospitalization and death were 24.9% (582/2341) and 4.6% (108/2341), respectively. Groups with the steepest declines had the highest rates of hospitalization and death within 90 days of dialysis initiation. The rate of eGFR decline is a valuable and readily available tool to stratify short-term (90 days) risk of hospitalization and death after the initiation of renal dialysis. More intense approaches are needed that apply models that identify high risks to potentially avert or reduce short-term hospitalization and death of patients with a severe and rapidly progressive chronic kidney disease.


Assuntos
Diálise Renal , Insuficiência Renal Crônica , Humanos , Diálise Renal/efeitos adversos , Insuficiência Renal Crônica/diagnóstico , Taxa de Filtração Glomerular , Hospitalização , Rim
3.
Adv Kidney Dis Health ; 30(1): 53-60, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36723283

RESUMO

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.


Assuntos
Injúria Renal Aguda , Inteligência Artificial , Humanos , Injúria Renal Aguda/diagnóstico , Medição de Risco/métodos , Fatores de Risco , Aprendizado de Máquina
4.
BMC Bioinformatics ; 24(1): 35, 2023 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-36732704

RESUMO

As new drug targets, human microbes are proven to be closely related to human health. Effective computational methods for inferring potential microbe-drug associations can provide a useful complement to conventional experimental methods and will facilitate drug research and development. However, it is still a challenging work to predict potential interactions for new microbes or new drugs, since the number of known microbe-drug associations is very limited at present. In this manuscript, we first constructed two heterogeneous microbe-drug networks based on multiple measures of similarity of microbes and drugs, and known microbe-drug associations or known microbe-disease-drug associations, respectively. And then, we established two feature matrices for microbes and drugs through concatenating various attributes of microbes and drugs. Thereafter, after taking these two feature matrices and two heterogeneous microbe-drug networks as inputs of a two-layer graph attention network, we obtained low dimensional feature representations for microbes and drugs separately. Finally, through integrating low dimensional feature representations with two feature matrices to form the inputs of a convolutional neural network respectively, a novel computational model named GACNNMDA was designed to predict possible scores of microbe-drug pairs. Experimental results show that the predictive performance of GACNNMDA is superior to existing advanced methods. Furthermore, case studies on well-known microbes and drugs demonstrate the effectiveness of GACNNMDA as well. Source codes and supplementary materials are available at: https://github.com/tyqGitHub/TYQ/tree/master/GACNNMDA.

5.
J Transl Med ; 21(1): 73, 2023 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-36737759

RESUMO

BACKGROUND: The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8+ T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model. METHODS: Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8+TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts. RESULTS: The percentage of PD-1+CD8+TILs, TIM-3+CD8+TILs, PD-L1+CD8+TILs, and PD-L1+CD8+PBLs and the density of PD-L1+CD8+TILs were independent risk factors, while the percentage of TIM-3+CD8+PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318-8.043, P = 0.006), with a higher density of PD-L1+CD8+TILs (P < 0.001) and percentage of PD-1+CD8+TILs (P = 0.017) and PD-L1+CD8+TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1+CD8+PBLs (P = 0.042), LAG-3+CD8+PBLs (P < 0.001), TIM-3+CD8+PBLs (P = 0.003), PD-L1+CD4+PBLs (P = 0.001), and LAG-3+CD4+PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017-0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3+CD8+PBLs, PD-L1+CD8+TILs and PD-1+CD8+TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively. CONCLUSION: We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.

6.
J Transl Med ; 21(1): 76, 2023 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-36737814

RESUMO

BACKGROUND: Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking. METHODS: Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342). RESULTS: Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44-1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset. CONCLUSIONS: Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

7.
Burns ; 2023 Jan 16.
Artigo em Inglês | MEDLINE | ID: mdl-36732103

RESUMO

BACKGROUND: Blood loss during burn excisional surgery remains an important factor as it is associated with significant comorbidity, mortality and longer length of stay. Blood loss is, among others, influenced by length of surgery, burn size, excision size and age. Most literature available is aimed at large burns and little research is available for small burns. Therefore, the goal of this study is to investigate blood loss and develop a prediction model to identify patient at risk for blood loss during burn excisional surgery ≤ 10% body surface area. STUDY DESIGN AND METHODS: This retrospective study included adult patients who underwent burn excisional surgery of ≤ 10% body surface area in the period 2013-2018. Duplicates, patients with missing data and delayed surgeries were excluded. Primary outcome was blood loss. A prediction model for per-operative blood loss (>250 ml) was built using a multivariable logistic regression analysis with stepwise backward elimination. Discriminative ability was assessed by the area under the ROC-curve in conjunction with optimism and calibration. RESULTS: In total 269 patients were included for analysis. Median blood loss was 50 ml (0-150) / % body surface area (BSA) excised and 0.28 (0-0.81) ml / cm2. Median burn size was 4% BSA and median excision size was 2% BSA. Blood loss of> 250 ml was present in 39% of patients. The model can predict blood loss> 250 ml based on %BSA excised, length of surgery and ASA-score with an AUC of 0.922 (95% CI 0.883 - 0.949) and an AUC after optimism correction of 0.915. The calibration curve showed an intercept of 0.0 (95% CI -0.36 to 0.36) with a slope of 1.0 (95% CI 0.78-1.22). CONCLUSION: Median blood loss during burn excisional surgery of ≤ 10% BSA is 50 ml / % BSA excised and 0.28 ml / cm2 excised. However, a substantial part of patients is at risk for higher blood loss. The prediction model can predict P(blood loss>250 ml) with an AUC of 0.922, based on expected length of surgery, ASA-score and size of excision. The model can be used to identify patients at risk for significant blood loss (>250 ml).

8.
J Med Virol ; 2023 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-36734063

RESUMO

Severe fever with thrombocytopenia syndrome (SFTS) is a life-threatening infectious disease caused by the SFTS virus (SFTSV). This study aimed to evaluate the predictive power of C-reactive protein to lymphocyte ratio (CLR) and establish an early-warning model for SFTS mortality. We retrospectively analysed hospitalised SFTS patients in six clinical centres from May 2011 to May 2022. The efficacy of CLR prediction was evaluated by the receiver operating characteristic (ROC) analysis. A nomogram was established and validated. 882 SFTS patients (median age 64 years, 48.5% male) were enrolled in this study, with a mortality rate of 17.8%. The area under the ROC curve (AUC) of CLR was 0.878 (95% CI: 0.850-0.903, P<0.001), which demonstrates high predictive strength. The least absolute shrinkage and selection operator (LASSO) regression selected seven potential predictors. Multivariate logistic regression analysis determined three independent risk factors, including CLR, to construct the nomogram. The performance of the nomogram displayed excellent discrimination and calibration, with significant net benefits in clinical uses. CLR is a brand-new predictor for SFTS mortality. The nomogram based on CLR can serve as a convenient tool for physicians to identify critical SFTS cases in clinical practice. This article is protected by copyright. All rights reserved.

9.
Accid Anal Prev ; 183: 106989, 2023 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-36724652

RESUMO

This paper compares the results found in successive accident prediction models developed at the national level for Norway. Over time, the models have become more comprehensive in terms of the roads and the variables included in them. It is found that traffic volume has consistently had the strongest association with the number of accidents. It explains nearly all the systematic variation in the number of accidents. The second most important variable has consistently been the speed limit of 50 km/h, which indicates an urban area (the default speed limit in urban areas in Norway is 50 km/h). This variable has become less important over time. Motorways (freeways) have consistently had a lower accident rate than other roads. The mean number of accidents per road section declined considerably from 1986 to 89 to 2010-15. Systematic variation in the number of accidents between road sections was greatly reduced. At present, the variation in the annual number of accidents between road sections is mostly random.

10.
Artigo em Inglês | MEDLINE | ID: mdl-36723990

RESUMO

CONTEXT: Gestational diabetes mellitus (GDM) is a common obstetric complication. Although early intervention could prevent the development of GDM, there was no consensus on early identification for women at high risk of GDM. OBJECTIVE: To develop a reliable prediction model of GDM in early pregnancy. METHODS: In this prospective cohort study, between 30 May 2021 and 13 August 2022, a total of 721 women were included at Women's Hospital, Zhejiang University School of Medicine. Participants were asked to complete oral glucose tolerance test (OGTT) during gestational week 7-14 for early prediction of GDM, and at week 24-28 for GDM diagnosis. Using OGTT results and baseline characteristics, logistic regression analysis was used to construct the prediction model. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, decision clinical analysis, and a nomogram were used for model performances assessment and visualization. Internal and external validation was performed to testify the stability of this model. RESULTS: According to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria in early OGTT, the mean (SD) age was 30.5 ± 3.7 in low-risk participants and 31.0 ± 3.9 in high-risk participants. The area under ROC curve (AUC) of the existing criteria at week 7-14 varied from 0.705 to 0.724. Based on maternal age, pre-pregnancy BMI, and results of early OGTT, the AUC of our prediction model was 0.8720, which was validated by both internal (AUC 0.8541) and external (AUC 0.8241) validation. CONCLUSIONS: The existing diagnostic criteria were unsatisfactory for early prediction of GDM. By combining early OGTT, we provided an effective prediction model of GDM in the first trimester.

11.
Quant Imaging Med Surg ; 13(1): 352-369, 2023 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-36620171

RESUMO

Background: The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis. Methods: Data used in this work were obtained from the osteoarthritis initiative (OAI). In this study, iROA was defined as a knee with a baseline Kellgren-Lawrence grade (KLG) of 0 or 1 that further progressed to KLG ≥2 during the follow-up visit. Intermediate-weighted turbo spin-echo knee MR images at the time of iROA diagnosis and 1 year prior were obtained. Five clinical characteristics-age, sex, body mass index, knee injury history, and knee surgery history-were obtained. A total of 604 knees were selected and matched (302 cases and 302 controls). A U-Net segmentation model was independently trained to automatically segment the IPFP. The prediction models were established in the training set (60%). Three main models were generated using (I) clinical characteristics; (II) radiomic features; (III) combined (clinical plus radiomic) features. Model performance was evaluated in an independent testing set (remaining 40%) using the area under the curve (AUC). Two secondary models were also generated using Hoffa-synovitis scores and clinical characteristics. Results: The comparison between the automated and manual segmentations of the IPFP achieved a Dice coefficient of 0.900 (95% CI: 0.891-0.908), which was comparable to that of experienced radiologists. The radiomic features model and the combined model yielded superior AUCs of 0.700 (95% CI: 0.630-0.763) and 0.702 (95% CI: 0.635-0.763), respectively. The DeLong test found no statistically significant difference between the receiver operating curves of the radiomic and combined models (P=0.831); however, both models outperformed the clinical model (P=0.014 and 0.004, respectively). Conclusions: Our results demonstrated that radiomic features of the IPFP are predictive of iROA 1 year prior to the diagnosis, suggesting that IPFP radiomic features can serve as an early quantitative prediction biomarker of iROA.

12.
CNS Neurosci Ther ; 2023 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-36650639

RESUMO

AIMS: Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). METHODS: We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. RESULTS: In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO-AIS, SVD performed better than regular clinical data, which is the opposite of LAA-AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO-AIS. [0.91 (0.84-0.97)]. CONCLUSIONS: Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO-AIS patients' prognosis.

13.
J Infect Public Health ; 16(3): 393-398, 2023 Jan 16.
Artigo em Inglês | MEDLINE | ID: mdl-36706468

RESUMO

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high mortality. Early identification of patients who may advance to critical stages is crucial. This investigation aimed to establish models to predict SFTS before it reaches the critical illness stage. METHODS: Between January 2016 and September 2022, 278 cases have been included in this study. There were 87 demographic and systemic chosen variables. For selecting the predictive variables from the cohort, the LASSO was utilized, and for identifying independent predictors, multivariate logistic regression was performed. Based on these factors, a nomogram was established for critical illness. Concordance index values, decision curve analysis and the area under the curve (AUC) were also examined. RESULTS: Multivariate logistic regression demonstrated the most important differentiating factors as;> 65 years old (P < 0.001, OR 3.388, 95 % CI 1.767-6.696), elevated serum PT (P = 0.011, OR 6.641, 95 % CI 1.584-31.934), elevated serum TT (P = 0.005, OR 3.384, 95 % CI 1.503-8.491), and elevated serum bicarbonate (P = 0.014, OR 0.242, 95 % CI 0.070-0.707). The C-index of the nomogram was 0.812 (95 % CI: 0.754-0.869), representing good discrimination. The model also showed excellent calibration. The AUC of the nomogram established based on four factors, as mentioned earlier, was 0.806. Furthermore, the model had the excellent net benefit, as revealed by the decision curve analysis. CONCLUSION: An accurate risk score system built on manifestations noted in patients with SFTS upon admission to hospital, might be advantageous in managing SFTS.

14.
Eur J Pediatr ; 2023 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-36708384

RESUMO

The aim was to develop a model to predict the adult height (AH) of idiopathic central precocious puberty (ICPP) girls who underwent gonadotropin-releasing hormone analog (GnRHa) treatment. Data analysis included 258 girls with ICPP. Among them, 101 girls who reached final AH (FAH) with GnRHa treatment were analyzed to verify three previous prediction models and develop a unique model based on multiple linear regression. The control group consisted of 41 untreated ICPP girls. Moreover, 116 girls treated with GnRHa who almost attained FAH were included for external validation. Based on our cohorts, all of the three previously published models underestimated the FAH with an R of 0.667, 0.793, and 0.664. The AH prediction model was built as follows: Calculated AH (cm) = 1.89630 * Height SDS + 2.29927 * Height SDS for bone age + 0.40776 * Target height + 100.16684 (R2 = 0.66 and adjusted R2 = 0.65). Internal validation showed a mean root mean squared error (RMSE) of 2.16 cm and a mean absolute error (MAE) of 1.64 cm. External validation showed that a significant error (> 1 SD) appeared only in 7 of 116 girls (6.0%). The model is displayed on the website: http://cpppredict.shinyapps.io/dynnomapp . CONCLUSION: A model for predicting the AH of girls with ICPP was developed incorporating the variables of height SDS, height SDS for bone age, and target height. The internal and external validation ensures an appropriate degree of discrimination and calibration of the prediction model. WHAT IS KNOWN: • Uncertainty prevails as how to predict the adult height of patients with central precocious puberty following gonadotropin-releasing hormone analog treatment. • Previous models for predicting adult height of girls with idiopathic central precocious puberty have not been proven translational to the Chinese population. WHAT IS NEW: • This study develops a new model for predicting the adult height of idiopathic central precocious puberty girls who underwent gonadotropin-releasing hormone analog treatment. • The internal and external validation assures a good degree of discrimination and calibration of the prediction model in this study.

15.
Artigo em Inglês | MEDLINE | ID: mdl-36674372

RESUMO

PURPOSE: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. METHODS: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. RESULTS: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. CONCLUSIONS: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.


Assuntos
Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/terapia , Terapia Neoadjuvante , Antígeno Ki-67 , Estudos Retrospectivos , Proteína Supressora de Tumor p53 , Biópsia
16.
Reprod Biol Endocrinol ; 21(1): 8, 2023 Jan 26.
Artigo em Inglês | MEDLINE | ID: mdl-36703171

RESUMO

STUDY QUESTION: To construct prediction models based on the Bayesian network (BN) learning method for the probability of fertilization failure (including low fertilization rate [LRF] and total fertilization failure [TFF]) in assisted reproductive technology (ART) treatment. A BN model was developed to predict TFF/LFR. The model showed relatively high calibration in external validation, which could facilitate the identification of risk factors for fertilization disorders and improve the efficiency of in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatment. WHAT IS KNOWN ALREADY: The prediction of TFF/LFR is very complex. Although some studies attempted to construct prediction models for TFF/LRF, most of the reported models were based on limited variables and traditional regression-based models, which are unsuitable for analyzing real-world clinical data. Therefore, none of the reported models have been widely used in routine clinical practice. To date, BN modeling analysis is a prominent and increasingly popular machine learning method that is powerful in dealing with dynamic and complex real-world data. STUDY DESIGN, SIZE, DURATION: A retrospective study was performed with 106,640 fresh embryo IVF/ICSI cycles from 2009 to 2019 in one of China's largest reproductive health centers. PARTICIPANTS/MATERIALS, SETTING, METHODS: A total of 106, 640 cycles were included in this study, including 97,102 controls, 4,339 LFR cases, and 5,199 TFF cases. Twenty-four predictors were initially included, including 13 female-related variables, five male-related variables, and six variables related to IVF/ICSI treatment. BN modeling analysis with tenfold cross-validation was performed to construct the predictive model for TFF/LFR. The receiver operating characteristic (ROC) curves and the corresponding area under the curves (AUCs) were used to evaluate the performance of the BN model. MAIN RESULTS AND THE ROLE OF CHANCE: All twenty-four predictors were first organized into seven hierarchical layers in a theoretical BN model, according to prior knowledge from previous literature and clinical practice. A machine-learning BN model was generated based on real-world clinical data, containing a total of eighteen predictors, of which the infertility type, ART method, and number of retrieved oocytes directly influence the probabilities of LFR/TFF. The prediction accuracy of the BN model was 91.7%. The AUC of the TFF versus control groups was 0.779 (95% CI: 0.766-0.791), with a sensitivity of 71.2% and specificity of 70.1%; the AUC of of TFF versus LFR groups was 0.807 (95% CI: 0.790-0.824), with a sensitivity of 49.0% and specificity of 99.0%. LIMITATIONS, REASON FOR CAUTION: First, our study was based on clinical data from a single center, and the results of this study should be further verified by external data. In addition, some critical data (e.g., the detailed IVF laboratory parameters of the sperm and oocytes used for insemination) were not available in this study, which should be given full consideration when further improving the performance of the BN model. WIDER IMPLICATIONS OF THE FINDINGS: Based on extensive clinical real-world data, we developed a BN model to predict the probabilities of fertilization failures in ART, which provides new clues for clinical decision-making support for clinicians in formulating personalized treatment plans and further improving ART treatment outcomes. STUDY FUNDING/COMPETING INTEREST(S): Dr. Y. Wang was supported by grants from the Beijing Municipal Science & Technology Commission (Z191100006619086). We declare that there are no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.


Assuntos
Fertilização In Vitro , Sêmen , Masculino , Feminino , Gravidez , Humanos , Estudos Retrospectivos , Teorema de Bayes , Fertilização In Vitro/métodos , Técnicas de Reprodução Assistida , Fertilização , Taxa de Gravidez
17.
Ann Fam Med ; 21(1): 11-18, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36690486

RESUMO

BACKGROUND: Urinary tract infection (UTI) symptoms are common in primary care, but antibiotics are appropriate only when an infection is present. Urine culture is the reference standard test for infection, but results take >1 day. A machine learning predictor of urine cultures showed high accuracy for an emergency department (ED) population but required urine microscopy features that are not routinely available in primary care (the NeedMicro classifier). METHODS: We redesigned a classifier (NoMicro) that does not depend on urine microscopy and retrospectively validated it internally (ED data set) and externally (on a newly curated primary care [PC] data set) using a multicenter approach including 80,387 (ED) and 472 (PC) adults. We constructed machine learning models using extreme gradient boosting (XGBoost), artificial neural networks, and random forests (RFs). The primary outcome was pathogenic urine culture growing ≥100,000 colony forming units. Predictor variables included age; gender; dipstick urinalysis nitrites, leukocytes, clarity, glucose, protein, and blood; dysuria; abdominal pain; and history of UTI. RESULTS: Removal of microscopy features did not severely compromise performance under internal validation: NoMicro/XGBoost receiver operating characteristic area under the curve (ROC-AUC) 0.86 (95% CI, 0.86-0.87) vs NeedMicro 0.88 (95% CI, 0.87-0.88). Excellent performance in external (PC) validation was also observed: NoMicro/RF ROC-AUC 0.85 (95% CI, 0.81-0.89). Retrospective simulation suggested that NoMicro/RF can be used to safely withhold antibiotics for low-risk patients, thereby avoiding antibiotic overuse. CONCLUSIONS: The NoMicro classifier appears appropriate for PC. Prospective trials to adjudicate the balance of benefits and harms of using the NoMicro classifier are appropriate.


Assuntos
Urinálise , Infecções Urinárias , Adulto , Humanos , Estudos Retrospectivos , Estudos Prospectivos , Microscopia , Infecções Urinárias/diagnóstico , Antibacterianos , Aprendizado de Máquina , Atenção Primária à Saúde/métodos
18.
BMC Pediatr ; 23(1): 47, 2023 01 28.
Artigo em Inglês | MEDLINE | ID: mdl-36707776

RESUMO

BACKGROUND: Intraventricular hemorrhage (IVH) is the most common type of brain injury in newborns, especially in newborns with Neonatal acute respiratory distress syndrome (ARDS). IVH can cause brain parenchyma damage and long-term neurological sequelae in children. Early identification and prevention of sequelae are essential. This study aims to establish a predictive nomogram for the early prediction of IVH in newborns with ARDS. METHODS: From 2019 to 2021, we collected data from 222 infants diagnosed with ARDS in the Department of Neonatology, First Affiliated Hospital of Xinjiang Medical University. Infants have been randomly assigned to the training set (n = 161) or the validation set (n = 61) at a ratio of 7:3. Variables were screened using the Least Absolute Contract and Selection Operator (LASSO) regression to create a risk model for IVH in infants with ARDS. The variables chosen in the LASSO regression model were used to establish the prediction model using multivariate logistic regression analysis. RESULTS: We recognized 4 variables as independent risk factors for IVH in newborns with ARDS via LASSO analysis, consisting of premature rupture of membranes (PROM), pulmonary surfactant (PS) dosage, PH1 and Arterial partial pressure of oxygen (PaO21). The C-Index for this dataset is 0.868 (95% CI: 0.837-0.940) and the C index in bootstrap verification is 0.852 respectively. The analysis of the decision curve shows that the model can significantly improve clinical efficiency in predicting IVH. We also provide a website based on the model and open it to users for free, so that the model can be better applied to clinical practice. CONCLUSION: In conclusion, the nomogram based on 4 factors shows good identification, calibration and clinical practicability. Our nomographs can help clinicians make clinical decisions, screen high-risk ARDS newborns, and facilitate early identification and management of IVH patients.


Assuntos
Ruptura Prematura de Membranas Fetais , Síndrome do Desconforto Respiratório do Recém-Nascido , Humanos , Recém-Nascido , Hemorragia Cerebral/complicações , Hemorragia Cerebral/diagnóstico , Nomogramas , Síndrome do Desconforto Respiratório do Recém-Nascido/complicações , Síndrome do Desconforto Respiratório do Recém-Nascido/diagnóstico , Fatores de Risco , Feminino , Gravidez
19.
BMC Surg ; 23(1): 25, 2023 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-36709302

RESUMO

AIM: The present study aimed to identify risk factors for venous thromboembolism (VTE) after pancreaticoduodenectomy (PD) and to develop and internally validate a predictive model for the risk of venous thrombosis. METHODS: We retrospectively collected data from 352 patients who visited our hospital to undergo PD from January 2018 to March 2022. The number of patients recruited was divided in an 8:2 ratio by using the random split method, with 80% of the patients serving as the training set and 20% as the validation set. The least absolute shrinkage and selection operator (Lasso) regression model was used to optimize feature selection for the VTE risk model. Multivariate logistic regression analysis was used to construct a prediction model by incorporating the features selected in the Lasso model. C-index, receiver operating characteristic curve, calibration plot, and decision curve were used to assess the accuracy of the model, to calibrate the model, and to determine the clinical usefulness of the model. Finally, we evaluated the prediction model for internal validation. RESULTS: The predictors included in the prediction nomogram were sex, age, gastrointestinal symptoms, hypertension, diabetes, operative method, intraoperative bleeding, blood transfusion, neutrophil count, prothrombin time (PT), activated partial thromboplastin time (APTT), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (AST/ALT), and total bilirubin (TBIL). The model showed good discrimination with a C-index of 0.827, had good consistency based on the calibration curve, and had an area under the ROC curve value of 0.822 (P < 0.001, 95%confidence interval:0.761-0.882). A high C-index value of 0.894 was reached in internal validation. Decision curve analysis showed that the VTE nomogram was clinically useful when intervention was decided at the VTE possibility threshold of 10%. CONCLUSION: The novel model developed in this study is highly targeted and enables personalized assessment of VTE occurrence in patients who undergo PD. The predictors are easily accessible and facilitate the assessment of patients by clinical practitioners.


Assuntos
Pancreaticoduodenectomia , Tromboembolia Venosa , Humanos , Pancreaticoduodenectomia/efeitos adversos , Tromboembolia Venosa/epidemiologia , Tromboembolia Venosa/etiologia , Estudos Retrospectivos , Fatores de Risco , Análise Fatorial , Nomogramas
20.
Risk Manag Healthc Policy ; 16: 55-68, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36714193

RESUMO

Background: An attempt at vaginal delivery by a woman who has previously had a cesarean section is known as a trial of labor after cesarean section. The most important issues are how to accurately anticipate successful vaginal birth after cesarean surgery and how to calculate the likelihood of success of vaginal birth after caesarean section that is suitable for women. Consequently, a tailored prediction of vaginal birth after caesarean section may result in a more effective counseling. Objective: To create a clinical risk score and prediction model for the success of vaginal birth following a previous caesarean section in women. Methods: A prognostic analysis was carried out at Felege Hiwot Comprehensive and Specialized Referral Hospital from 30 February 2017 to 30 March 2021. R statistical programming language version 4.0 was used for analysis once the data had been coded and entered into Epidata, version 3.02. Significant factors (P< 0.05) were kept in the backward multivariable logistic regression model, and variables with (P<0.25) from the bi-variable logistic regression analysis were also added. Results: After a cesarean section, 67% of women were successful in giving birth vaginally. Previous successful vaginal birth after cesarean surgery, rupture of the membranes, and initiation time of ANC, the beginning of labor, parity and time since the previous delivery were remained in the final multivariable prediction model. The AUC of the model was 0.748 (95% CI: 0.714-0.781). Conclusion: Overall, this study demonstrated the likelihood of predicting vaginal birth utilizing the ideal confluence of parity, membrane rupture, and onset of labor, prior history of VBAC, inter-delivery gap, and ANC beginning time. Sixty-seven percent of VBACs were successful. As a result, this model may aid in identifying pregnant women who are candidates for VBAC and who have a better likelihood of success.

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