Clinical risk prediction model and external validation of positive surgical margin in laparoscopic radical prostatectomy based on MRI lesion location.

OBJECTIVE: To clarify the composition of lesions in different magnetic resonance imaging (MRI) partitions of positive surgical margins (PSM) after laparoscopic radical prostatectomy, explore the influence of lesion location on PSM, and construct a clinical prediction model to predict the risk of PSM. MATERIALS AND METHODS: This retrospective cohort study included 309 patients who underwent laparoscopic radical prostatectomy from 2018 to 2021 in our center was performed. 129 patients who met the same criteria from January to September 2022 were external validation cohorts. RESULTS: The incidence of PSM in transition zone (TZ) lesions was higher than that in peripheral zone (PZ) lesions. The incidence of PSM in the middle PZ was lower than that in other regions. Prostate specific antigen (PSA), clinical T-stage, the number of positive cores, international society of urological pathology (ISUP) grade (biopsy), MRI lesion location, extracapsular extension, seminal vesicle invasion (SVI), pseudo-capsule invasion (PCI), long diameter of lesions, lesion volume, lesion volume ratio, PSA density were related to PSM. MRI lesion location and PCI were independent risk factors for PSM. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a clinical prediction model for PSM, including five variables: the number of positive cores, SVI, MRI lesion location, long diameter of lesions, and PSA. CONCLUSION: The positive rate of surgical margin in middle PZ was significantly lower than that in other regions, and MRI lesion location was an independent risk factor for PSM.

Comparison of the CIPHER prognostic model with the existing scores in predicting severe maternal outcomes during intensive care unit admission.

OBJECTIVE: To compare the performance of the Collaborative Integrated Pregnancy High-Dependency Estimate of Risk (CIPHER) model in predicting maternal death and near-miss morbidity (Severe Maternal Outcome [SMO]) with the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the Simplified Acute Physiology Score (SAPS) III scores. METHODS: A retrospective and a prospective study was conducted at two centers in Brazil. For each score, area under curve (AUC) was used and score calibration was assessed using the Hosmer-Lemeshow statistic (H-L) test and the standardized mortality ratio (SMR). RESULTS: A cohort of 590 women was analyzed. A SMO was observed in 216 (36.6%) women. Of these, 13 (2.2%) were maternal deaths and 203 (34.4%) met one or more maternal near-miss criteria. The CIPHER model did not show significant diagnostic ability (AUC 0.52) and consequently its calibration was poor (H-L P < 0.05). The SAPS III had the best performance (AUC 0.77, H-L P > 0.05 and SMR 0.85). CONCLUSION: The performance of the CIPHER model was lower compared to the other scores. Since the CIPHER model is not ready for clinical use, the SAPS III score should be considered for the prediction of SMO.

Morbimortality assessment in abdominal surgery: are we predicting or overreacting?

BACKGROUND: High-risk surgical procedures represent a fundamental part of general surgery practice due to its significant rates of morbidity and mortality. Different predictive tools have been created in order to quantify perioperative morbidity and mortality risk. POSSUM (Physiological and Operative Severity Score for the enumeration of Mortality and morbidity) is one of the most widely validated predictive scores considering physiological and operative variables to precisely define morbimortality risk. Nevertheless, seeking greater accuracy in predictions P-POSSUM was proposed. We aimed to compare POSSUM and P-POSSUM for patients undergoing abdominal surgery. METHODS: A retrospective observational study with a prospective database was conducted. Patients over 18 years old who complied with inclusion criteria between 2015 and 2016 were included. Variables included in the POSSUM and P-POSSUM Scores were analyzed. Descriptive statistics of all study parameters were provided. The analysis included socio-demographic data, laboratory values ​​, and imaging. Bivariate analysis was performed. RESULTS: 350 Patients were included in the analysis, 55.1% were female. The mean age was 55.9 ± 20.4 years old. POSSUM revealed a moderated index score in 61.7% of the patients, mean score of 12.85 points ± 5.61. 89.1% of patients had no neoplastic diagnosis associated. Overall morbidity and mortality rate was 14.2% and 7.1%. P-POSSUM could predict more precisely mortality (p < 0.00). CONCLUSIONS: The POSSUM score is likely to overestimate the risk of morbidity and mortality in patients with high/moderate risk, while the P-POSSUM score seems to be a more accurate predictor of mortality risk. Further studies are needed to confirm our results.

Risk-Prediction Model for Transfusion of Erythrocyte Concentrate During Extracorporeal Circulation in Coronary Surgery

Abstract Introduction: Our objective was to identify preoperative risk factors and to develop and validate a risk-prediction model for the need for blood (erythrocyte concentrate [EC]) transfusion during extracorporeal circulation (ECC) in patients undergoing coronary artery bypass grafting (CABG). Methods: This is a retrospective observational study including 530 consecutive patients who underwent isolated on-pump CABG at our Centre over a full two-year period. The risk model was developed and validated by logistic regression and bootstrap analysis. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow (H-L) test, respectively. Results: EC transfusion during ECC was required in 91 patients (17.2%). Of these, the majority were transfused with one (54.9%) or two (41.8%) EC units. The final model covariates (reported as odds ratios; 95% confidence interval) were age (1.07; 1.02-1.13), glomerular filtration rate (0.98; 0.96-1.00), body surface area (0.95; 0.92-0.98), peripheral vascular disease (3.03; 1.01-9.05), cerebrovascular disease (4.58; 1.29-16.18), and hematocrit (0.55; 0.48-0.63). The risk model developed has an excellent discriminatory power (AUC: 0,963). The results of the H-L test showed that the model predicts accurately both on average and across the ranges of deciles of risk. Conclusions: A risk-prediction model for EC transfusion during ECC was developed, which performed adequately in terms of discrimination, calibration, and stability over a wide spectrum of risk. It can be used as an instrument to provide accurate information about the need for EC transfusion during ECC, and as a valuable adjunct for local improvement of clinical practice. OR=odds ratio Key Question: What is the risk of the need for use of erythrocyte concentrate (EC) during cardiopulmonary bypass? Key Findings: Risk factors with the greatest prediction for EC transfusion. Take-Home Message: The implementation of this model would be an important step in optimizing and improving the quality of surgery.

Gingival Crevicular Placental Alkaline Phosphatase Is an Early Pregnancy Biomarker for Pre-Eclampsia.

Early and innovative diagnostic strategies are required to predict the risk of developing pre-eclampsia (PE). The purpose of this study was to evaluate the performance of gingival crevicular fluid (GCF) placental alkaline phosphatase (PLAP) concentrations to correctly classify women at risk of PE. A prospectively collected, retrospectively stratified cohort study was conducted, with 412 pregnant women recruited at 11-14 weeks of gestation. Physical, obstetrical, and periodontal data were recorded. GCF and blood samples were collected for PLAP determination by ELISA assay. A multiple logistic regression classification model was developed, and the classification efficiency of the model was established. Within the study cohort, 4.3% of pregnancies developed PE. GCF-PLAP concentration was 3- to 6-fold higher than in plasma samples. GCF-PLAP concentrations and systolic blood pressure were greater in women who developed PE (p = 0.015 and p < 0.001, respectively). The performance of the multiparametric model that combines GCF-PLAP concentration and the levels of systolic blood pressure (at 11-14 weeks gestation) showed an association of systolic blood pressure and GCF-PLAP concentrations with the likelihood of developing PE (OR:1.07; 95% CI 1.01-1.11; p = 0.004 and OR:1.008, 95% CI 1.000-1.015; p = 0.034, respectively). The model had a sensitivity of 83%, a specificity of 72%, and positive and negative predictive values of 12% and 99%, respectively. The area under the receiver operating characteristic (AUC-ROC) curve was 0.77 and correctly classified 72% of PE pregnancies. In conclusion, the multivariate classification model developed may be of utility as an aid in identifying pre-symptomatic women who subsequently develop PE.

Risk-Prediction Model for Transfusion of Erythrocyte Concentrate During Extracorporeal Circulation in Coronary Surgery.

INTRODUCTION: Our objective was to identify preoperative risk factors and to develop and validate a risk-prediction model for the need for blood (erythrocyte concentrate [EC]) transfusion during extracorporeal circulation (ECC) in patients undergoing coronary artery bypass grafting (CABG). METHODS: This is a retrospective observational study including 530 consecutive patients who underwent isolated on-pump CABG at our Centre over a full two-year period. The risk model was developed and validated by logistic regression and bootstrap analysis. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow (H-L) test, respectively. RESULTS: EC transfusion during ECC was required in 91 patients (17.2%). Of these, the majority were transfused with one (54.9%) or two (41.8%) EC units. The final model covariates (reported as odds ratios; 95% confidence interval) were age (1.07; 1.02-1.13), glomerular filtration rate (0.98; 0.96-1.00), body surface area (0.95; 0.92-0.98), peripheral vascular disease (3.03; 1.01-9.05), cerebrovascular disease (4.58; 1.29-16.18), and hematocrit (0.55; 0.48-0.63). The risk model developed has an excellent discriminatory power (AUC: 0,963). The results of the H-L test showed that the model predicts accurately both on average and across the ranges of deciles of risk. CONCLUSIONS: A risk-prediction model for EC transfusion during ECC was developed, which performed adequately in terms of discrimination, calibration, and stability over a wide spectrum of risk. It can be used as an instrument to provide accurate information about the need for EC transfusion during ECC, and as a valuable adjunct for local improvement of clinical practice. Key Findings: Risk factors with the greatest prediction for EC transfusion. Take-Home Message: The implementation of this model would be an important step in optimizing and improving the quality of surgery.

Evaluación de los factores de riesgo que predisponen a la hipertensión arterial a través de un modelo de regresión logística / Assessment of risk factors that predispose to hypertension through a logistic regression model

La hipertensión arterial (HTA) representa uno de los factores de riesgo que más contribuye a la enfermedad cardiovascular y actualmente se desarrollan modelos de predicción de riesgo a padecerla. El objetivo de esta investigación fue evaluar la reproducibilidad de un modelo de regresión logística en una población del estado Carabobo, Venezuela, así como la introducción de nuevas variables que mejoren dicho modelo. A 202 pacientes se les evaluó distintos factores de riesgo de HTA con los cuales se evaluó la reproducibilidad de un modelo ya planteado. Posteriormente se evaluó la introducción de nuevas variables al modelo que pudieran mejorar el mismo, utilizando el método del paso a paso de regresión logística. El modelo de predicción de riesgo que sirve como base a este estudio incorpora 3 variables: Presión arterial sistólica (PAS), edad e índice de masa corporal (IMC), de los cuales en este trabajo sólo edad e IMC resultaron significativas (p <0,000 y p <0,012 respectivamente). Una segunda regresión logística adicional, evaluó la introducción de nuevas variables al estudio, donde solo el fenotipo de cintura hipertrigliceridemia (CHT) contribuye a mejorar el modelo predictivo de la HTA. Por tanto, se encontró reproducibilidad parcial del modelo de predicción de riesgo de HTA, además de mejorar el mismo, al añadir la variable fenotipo de CHT. Se recomienda realizar nuevas investigaciones en otras poblaciones venezolanas así como estudios que involucren otras covariables clínicas.

High blood pressure (hypertension) is one of the risk factors that contribute most to cardiovascular disease. Adequate risk prediction models are needed to address prevention. The objective of this research was to evaluate the reproducibility of a logistic regression model in a population of Carabobo, Venezuela, and the introduction of new variables to improve the model. A total of 202 patients were assessed various risk factors of hypertension with which the reproducibility of an already proposed model was evaluated. Later, the introduction of new variables to the model that could improve it by using the stepwise logistic regression method was evaluated. The model risk prediction that serves as the basis of this study incorporates three variables: systolic blood pressure (SBP), age and body mass index (BMI), from which, in this work, only age and BMI were significant (p-value <0.000 and p <0.012 respectively). A second additional logistic regression assessed the introduction of new variables to the study, where only hypertriglyceridemic waist (HTW) phenotype helps to improve the predictive model of hypertension. Therefore, partial reproducibility of risk prediction model of hypertension was found, in addition to improving it by adding the variable HTW phenotype. We recommend further research in other Venezuelan populations and studies involving other clinical covariates.

A prospective and retrospective analysis of smoking behavior changes in ever smokers with high risk for lung cancer from New Mexico and Pennsylvania.

Cigarette smoking is the leading preventable cause of death worldwide. The aim of this study is to conduct a prospective and retrospective analysis of smoking behavior changes in the Lovelace Smokers Cohort (LSC) and the Pittsburgh Lung Screening Study cohort (PLuSS). Area under the curve (AUC) for risk models predicting relapse based on demographic, smoking, and relevant clinical variables was 0.93 and 0.79 in LSC and PLuSS, respectively. The models for making a quit attempt had limited prediction ability in both cohorts (AUC≤0.62). We identified an ethnic disparity in adverse smoking behavior change that Hispanic smokers were less likely to make a quit attempt and were more likely to relapse after a quit attempt compared to non-Hispanic Whites. SNPs at 15q25 and 11p14 loci were associated with risk for smoking relapse in the LSC. Rs6495308 at 15q25 has a large difference in minor allele frequency between non-Hispanic Whites and Hispanics (0.46 versus 0.23, P<0.0001) and was associated with risk for ever relapse at same magnitude between the two ethnic groups (OR=1.36, 95% CI=1.10 to 1.67 versus 1.59, 95% CI=1.00 to 2.53, P=0.81). In summary, the risk prediction model established in LSC and PLuSS provided an excellent to outstanding distinguishing for abstainers who will or will not relapse. The ethnic disparity in adverse smoking behavior between Hispanics and non-Hispanic Whites may be at least partially explained by the sequence variants at 15q25 locus that contains multiple nicotine acetylcholine receptors.