A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19.

Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Testosterone Recovery after Androgen Deprivation Therapy in Prostate Cancer: Building a Predictive Model.

PURPOSE: To analyze the variability, associated actors, and the design of nomograms for individualized testosterone recovery after cessation of androgen deprivation therapy (ADT). MATERIALS AND METHODS: A longitudinal study was carried out with 208 patients in the period 2003 to 2019. Castrated and normogonadic testosterone levels were defined as 0.5 and 3.5 ng/mL, respectively. The cumulative incidence curve described the recovery of testosterone. Univariate and multivariate analyzes were performed to predict testosterone recovery with candidate prognostic factors prostate-specific antigen at diagnosis, clinical stage, Gleason score from biopsy, age at cessation of ADT, duration of ADT, primary therapy and use of LHRH (luteinizing hormone-releasing hormone) agonists. RESULTS: The median follow-up duration in the study was 80 months (interquartile range, 49-99 mo). Twenty-five percent and 81% of patients did not recover the castrate and normogonadic levels, respectively. Duration of ADT and age at ADT cessation were significant predictors of testosterone recovery. We built two nomograms for testosterone recovery at 12, 24, 36, and 60 months. The castration recovery model had good calibration. The C-index was 0.677, with area under the receiver operating characteristic curve (AUC-ROC) of 0.736, 0.783, 0.782, and 0.780 at 12, 24, 36, and 60 months, respectively. The normogonadic recovery model overestimated the higher values of probability of recovery. The Cindex was 0.683, with AUC values of 0.812, 0.711, 0.708 and 0.693 at 12, 24, 36, and 60 months, respectively. CONCLUSIONS: Depending on the age of the patient and the length of treatment, clinicians may stop ADT and the castrated testosterone level will be maintained or, if the course of treatment has been short, we can estimate if it will return to normogonadic levels.

Personalized Model to Predict Small for Gestational Age at Delivery Using Fetal Biometrics, Maternal Characteristics, and Pregnancy Biomarkers: A Retrospective Cohort Study of Births Assisted at a Spanish Hospital.

Small for gestational age (SGA) is defined as a newborn with a birth weight for gestational age < 10th percentile. Routine third-trimester ultrasound screening for fetal growth assessment has detection rates (DR) from 50 to 80%. For this reason, the addition of other markers is being studied, such as maternal characteristics, biochemical values, and biophysical models, in order to create personalized combinations that can increase the predictive capacity of the ultrasound. With this purpose, this retrospective cohort study of 12,912 cases aims to compare the potential value of third-trimester screening, based on estimated weight percentile (EPW), by universal ultrasound at 35−37 weeks of gestation, with a combined model integrating maternal characteristics and biochemical markers (PAPP-A and ß-HCG) for the prediction of SGA newborns. We observed that DR improved from 58.9% with the EW alone to 63.5% with the predictive model. Moreover, the AUC for the multivariate model was 0.882 (0.873−0.891 95% C.I.), showing a statistically significant difference with EPW alone (AUC 0.864 (95% C.I.: 0.854−0.873)). Although the improvements were modest, contingent detection models appear to be more sensitive than third-trimester ultrasound alone at predicting SGA at delivery.

Statistical characterization of a good biomarker in oncology. / Características bioestadísticas de un buen biomarcador en Oncología.

OBJECTIVE: The  aim of this article is to review and illustrate the attributes that analyze the performance of a predictive model, suchas discrimination, calibration and clinical utility. MATERIAL AND METHODS: To illustrate a biomarkervalidation process, we analyzed 216 patientsrecruited in the Miguel Servet University Hospital Zaragoza, Spain. The outcome of the study was clinicallysignificant prostate cancer (Gleason ≥ 7). A newbiomarker was built using logistic regression modelfrom age, prostate-specific antigen, prostate volumeand digital rectal exam variables. To analyze the discriminationability, the receiver operating characteristiccurve, its area under the curve (AUC), and Youdenindex were estimated. In addition, the calibration wasanalyzed through calibration curve, intercept and slope;and the clinical utility was studied by means of decisionand clinical utility curves. RESULTS: The discrimination ability was good:AUC 0.790 (0.127-0.853 95% C.I.), Youden index cutoffpoint 0.431 (specificity 0.811, sensitivity 0.697).The Intercept was 0 and Slope 1 showing a perfect calibration.Decision curve showed good net benefit in athreshold probability range 25%-80%. Clinical utilitycurve showed that for a 18% cutoff point, a minimum4.5% of CsPCa patients are wrongly classified belowthe cutoff point, saving 18.5% biopsies. CONCLUSIONS: A complete validation process isnecessary to analyze the performance of a biomarkerin oncology, based on their discrimination ability, theconcordance between predicted and actual occurrenceof the outcome, and its applicability in clinical practice.

OBJETIVO: El objetivo principal de esteartículo es revisar e ilustrar las propiedades para analizarel desempeño de un modelo predictivo, que sonla discriminación, calibración y utilidad clínica.MATERIAL Y MÉTODOS: Para ilustrar un procesode validación de biomarcadores, analizamos 216 pacientesreclutados en el Hospital Universitario MiguelServet, Zaragoza, España. El objetivo a predecir en elestudio fue un cáncer de próstata clínicamente significativo(Gleason ≥ 7). Se construyó un nuevo biomarcadorutilizando un modelo de regresión logísticausando la edad, el antígeno prostático específico, elvolumen de la próstata y el tacto rectal como variablespredictoras. Para analizar la capacidad de discriminaciónse estimó la curva característica de funcionamientodel receptor, su área bajo la curva (AUC) y elíndice de Youden. Además, la calibración se analizómediante curva de calibración, intersección y pendiente;y la utilidad clínica se estudió mediante curvasde decisión y utilidad clínica. RESULTADOS: La capacidad de discriminación fuebuena: AUC 0,790 (0,127-0,853 IC del 95%), punto decorte del índice de Youden 0,431 (especificidad 0,811,sensibilidad 0,697). La intersección fue 0 y la pendiente1, mostrando una calibración perfecta. La curva dedecisión muestra un buen beneficio neto en un rangode probabilidad del 25% al 80%. La curva de utilidadclínica mostró que para un punto de corte del 18%, seproduce un mínimo del 4,5% de los pacientes con CsPCaclasificados incorrectamente por debajo del puntode corte, ahorrando un 18,5% de biopsias. CONCLUSIONES: Es necesario un proceso de validacióncompleto para analizar el desempeño de un biomarcadoren oncología, en función de su capacidad dediscriminación, la concordancia entre las prediccionesque proporciona el marcador y la ocurrencia real delevento, y su aplicabilidad en la práctica clínica.

Características bioestadísticas de un buen biomarcador en oncología / Statistical characterization of a good biomarker in oncology

OBJETIVO: El objetivo principal de esteartículo es revisar e ilustrar las propiedades para analizar el desempeño de un modelo predictivo, que sonla discriminación, calibración y utilidad clínica.MATERIAL Y MÉTODOS: Para ilustrar un procesode validación de biomarcadores, analizamos 216 pacientes reclutados en el Hospital Universitario MiguelServet, Zaragoza, España. El objetivo a predecir en elestudio fue un cáncer de próstata clínicamente significativo (Gleason ≥ 7). Se construyó un nuevo biomarcador utilizando un modelo de regresión logísticausando la edad, el antígeno prostático específico, elvolumen de la próstata y el tacto rectal como variablespredictoras. Para analizar la capacidad de discriminación se estimó la curva característica de funcionamiento del receptor, su área bajo la curva (AUC) y elíndice de Youden. Además, la calibración se analizómediante curva de calibración, intersección y pendiente; y la utilidad clínica se estudió mediante curvasde decisión y utilidad clínica. RESULTADOS: La capacidad de discriminación fuebuena: AUC 0,790 (0,127-0,853 IC del 95%), punto decorte del índice de Youden 0,431 (especificidad 0,811,sensibilidad 0,697). La intersección fue 0 y la pendiente 1, mostrando una calibración perfecta. La curva dedecisión muestra un buen beneficio neto en un rangode probabilidad del 25% al 80%. La curva de utilidadclínica mostró que para un punto de corte del 18%, seproduce un mínimo del 4,5% de los pacientes con CsPCa clasificados incorrectamente por debajo del puntode corte, ahorrando un 18,5% de biopsias.CONCLUSIONES: Es necesario un proceso de validación completo para analizar el desempeño de un biomarcador en oncología, en función de su capacidad dediscriminación, la concordancia entre las prediccionesque proporciona el marcador y la ocurrencia real delevento, y su aplicabilidad en la práctica clínica. (AU)

lustrate the attributes that analyze theperformance of a predictive model, such as discrimination, calibration and clinical utility.MATERIAL AND METHODS: To illustrate a biomarker validation process, we analyzed 216 patientsrecruited in the Miguel Servet University Hospital, Zaragoza, Spain. The outcome of the study was clinically significant prostate cancer (Gleason ≥ 7). A newbiomarker was built using logistic regression modelfrom age, prostate-specific antigen, prostate volumeand digital rectal exam variables. To analyze the discrimination ability, the receiver operating characteristic curve, its area under the curve (AUC), and Youdenindex were estimated. In addition, the calibration wasanalyzed through calibration curve, intercept and slope; and the clinical utility was studied by means of decision and clinical utility curves.RESULTS: The discrimination ability was good:AUC 0.790 (0.127-0.853 95% C.I.), Youden index cutoff point 0.431 (specificity 0.811, sensitivity 0.697).The Intercept was 0 and Slope 1 showing a perfect calibration. Decision curve showed good net benefit in athreshold probability range 25%-80%. Clinical utilitycurve showed that for a 18% cutoff point, a minimum4.5% of CsPCa patients are wrongly classified belowthe cutoff point, saving 18.5% biopsies.CONCLUSIONS: A complete validation process isnecessary to analyze the performance of a biomarkerin oncology, based on their discrimination ability, theconcordance between predicted and actual occurrenceof the outcome, and its applicability in clinical practice. (AU)

Reduced Growth in Non-Small for Gestational Age Fetuses from 35 Weeks of Gestation to Birth and Perinatal Outcomes.

OBJECTIVE: This study aimed to assess reduced fetal growth between 35 weeks of gestation and birth in non-small for gestational age fetuses associated with adverse perinatal outcomes (APOs). MATERIAL AND METHOD: It is a retrospective cohort study of 9,164 non-small for gestational age fetuses estimated by ultrasound at 35 weeks. The difference between the birth weight percentile and the estimated percentile weight (EPW) at 35 weeks of gestation was calculated, and we studied the relationship of this difference with the appearance of APO. APOs were defined as cesarean or instrumental delivery rates for nonreassuring fetal status, 5-min Apgar score <7, arterial cord blood pH <7.10, and stillbirth. Fetuses that exhibited a percentile decrease between both moments were classified into 6 categories according to the amount of percentile decrease (0.01-10.0, 10.01-20.0, 20.01-30.0, 30.01-40.0, 40.01-50.0, and >50.0 percentiles). It was evaluated whether the appearance of APO was related to the amount of this percentile decrease. Relative risk (RR) was calculated in these subgroups to predict APOs in general and for each APO in particular. Receiver operating characteristic and area under curves (AUC) for the difference in the percentile was calculated, used as a continuous parameter in the entire study population. RESULTS: The median gestational age at delivery in uncomplicated pregnancies was 40.0 (39.1-40.7) and in pregnancies with APOs 40.3 (49.4-41.0), p < 0.001. The prevalence of APOs was greater in the group of fetuses with a decrease in percentile (7.6%) compared to those with increased percentile (4.8%) (p < 0.001). The RR was 1.63 (95% CI: 1.365-1.944, p < 0.001). Although the differences were significant in all decreased percentile groups, RRs were significantly higher when decreased growth values were >40 points (RR: 2.036, 95% CI: 1.581-2.623, p < 0.001). The estimated value of the AUC for percentile decrease was 0.58 (0.56-0.61, p < 0.001). CONCLUSION: Fetuses with a decrease in the EPW between the ultrasound at 35 weeks of gestation and birth have a higher risk of APOs, being double in fetuses with a decrease of >40 percentile points.

Teaching Urology to Undergraduates: A Prospective Survey of What General Practitioners Need to Know.

BACKGROUND: Higher education training in Medicine has considerably evolved in recent years. One of its main goals has been to ensure the training of students as future adequately qualified general practitioners (GPs). Tools need to be developed to evaluate and improve the teaching of Urology at the undergraduate level. Our objective is to identify the knowledge and skills needed in Urology for the real clinical practice of GPs. METHODS: An anonymous self-administered survey was carried out among GPs of Primary Care and Emergencies which sought to evaluate urological knowledge and necessary urological skills. The results of the survey were exported and descriptive statistics were performed using IBM SPSS Statistics version 19.0. RESULTS AND LIMITATIONS: A total of 127 answers were obtained, in which 'Urological infections', 'Renal colic', 'PSA levels and screening for prostate cancer', 'Benign prostatic hyperplasia', 'Hematuria', 'Scrotal pain', 'Prostate cancer diagnosis', 'Bladder cancer diagnosis', 'Urinary incontinence', and 'Erectile dysfunction' were rated as Very high or High formative requirements (>75%). Regarding urological skills, 'Abdominal examination', 'Interpretation of urinalysis', 'Digital rectal examination', 'Genital examination', and 'Transurethral catheterization' were assessed as needing Very high or High training in more than 80% of the surveys. The relevance of urological pathology in clinical practice was viewed as Very high or High in more than 80% of the responses. CONCLUSIONS: This study has shown helpful results to establish a differentiated prioritization of urological knowledge and skills in Primary Care and Emergencies. Efforts should be aimed at optimizing the teaching in Urology within the Degree of Medicine which consistently ensures patients' proper care by future GPs.

Prediction of Late-Onset Small for Gestational Age and Fetal Growth Restriction by Fetal Biometry at 35 Weeks and Impact of Ultrasound-Delivery Interval: Comparison of Six Fetal Growth Standards.

Small-for-gestational-age (SGA) infants have been associated with increased risk of adverse perinatal outcomes (APOs). In this work, we assess the predictive ability of the ultrasound-estimated percentile weight (EPW) at 35 weeks of gestational age to predict late-onset SGA and APOs, according to six growth standards, and whether the ultrasound-delivery interval influences the detection rate. To this purpose, we analyze a retrospective cohort study of 9585 singleton pregnancies. EPWs at 35 weeks were calculated to the customized Miguel Servet University Hospital (MSUH) and Figueras standards and the non-customized MSUH, Fetal Medicine Foundation (FMF), INTERGROWTH-21st, and WHO standards. As results of our analysis, for a 10% false positive rate, the detection rates for SGA ranged between 48.9% with the customized Figueras standard (AUC 0.82) and 60.8% with the non-customized FMF standard (AUC 0.87). Detection rates to predict SGA by ultrasound-delivery interval (1-6 weeks) show higher detection rates as intervals decrease. APOs detection rates ranged from 27.0% with FMF to 7.9% with the Figueras standard. In conclusion, the ability of EPW to predict SGA at 35 weeks is good for all standards, and slightly better for non-customized standards. The APO detection rate is significantly greater for non-customized standards.

Pregestational body mass index, trimester-specific weight gain and total gestational weight gain: how do they influence perinatal outcomes?

OBJECTIVE: To investigate the association between pre-gestational body mass index (BMI), total gestational weight gain (GWG), and/or trimester-specific weight gain (GWGT) with adverse maternal or perinatal outcomes (AMPOs). MATERIALS AND METHODS: Maternal clinical characteristics and pregnancy and perinatal outcomes were used to predict AMPOs. The predictive ability of BMI, GWG, or GWGT for AMPOs was analyzed using the area under the curve (AUC). Logistic regression models in a univariate and multivariate analysis were performed to estimate the odds ratios (OR) and 95% confidence intervals (CI) to predict maternal outcomes (pregnancy-induced hypertension, preeclampsia or gestational diabetes mellitus) and perinatal outcomes (small for gestational age, large for gestational age, 5-min Apgar score, admission to neonatal intensive care unit or umbilical cord pH <7.15). RESULTS: Women with AMPOs (n = 293) were younger with higher rate of nulliparity (p < .001) and with lower height (p = .018) as compared to controls (n = 134). In the univariate study, GWGT in third trimester was associated with double risk of pregnancy-induced hypertension (OR 2.00; 95% CI, 1.01-3.97). Nonetheless, third-trimester GWG and total GWG have a negative relationship with gestational diabetes mellitus OR 0.32 (95% CI, 0.18-0.58) and OR 0.35 (95% CI, 0.21-0.59), respectively. Women with greater overall and in second trimester, GWG have a lower risk of having SGA neonates, OR 0.62 (95% CI, 0.39-0.98) and OR 0.60 (95% CI, 0.37-0.98), respectively. In the multivariate study, pre-gestational BMI is strongly related to the development of preeclampsia and the area under the curve (AUC) of the combination of pre-gestational BMI and total weight gain was 0.832 (95% CI, 0.63-0.81) for preeclampsia and 0.719 (95% CI, 0.71-0.94) for gestational diabetes mellitus. CONCLUSION: Our results suggest than timing of gestational weight gain influence in maternal and perinatal outcomes. Pre-gestational BMI is a determinant of preeclampsia, maternal weight gain in the third trimester is a determinant of pregnancy-induced hypertension and the increase in total GWG reduces the risk of gestational diabetes mellitus and small for gestational age.

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.

BACKGROUND: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. METHODS: A case-control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. RESULTS: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. CONCLUSIONS: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

Clinical utility of thrombophilia, anticoagulant treatment, and maternal variables as predictors of placenta-mediated pregnancy complications: an extensive analysis.

Objective: The objective of this study is to analyze the usefulness of thrombophilia and antithrombotic drugs in combination with materno-fetal characteristics to generate a predictive model of placenta-mediated pregnancy complications (PMPC) for counseling treatment.Methods: A retrospective analysis was performed in women with singleton pregnancy that required a thrombophilia study, including 222 patients with unknown cause PMPC and 151 women with no complications at current pregnancy in Hospital Clínico Universitario, Lozano, Blesa, Zaragoza, Spain. Chi-squared and Mann-Whitney test were applied to analyze univariate risk factors. Multivariate analysis was performed using logistic regression model with candidate variables: maternal characteristics, obstetric history, thrombophilia, and treatment with low-molecular-weight heparin (LMWH) and/or with acid acetylsalicylic (ASA). The calibration, discrimination, and best cutoff point for the clinical application of the model was analyzed.Results: Maternal characteristics showed differences in median body mass index (BMI), odds ratio (OR): 0.4, smoking habit, OR: 8.5, and hypertension, OR: 11.4, appearing all of them as risk factors. In our study, a prior pregnancy that ended in a child alive was a protective factor OR: 0.02-0.4, and having a previous preterm child was a strong risk factor OR: 4.2. Thrombophilia was not a risk factor. Patients under LMWH treatment (15%) and/or ASA (6.2%) had better pregnancy outcomes, showing both as protective factors: ASA OR: 0.32 and LMWH OR: 0.16. The model has an AUC value of 0.847, with good calibration. A nomogram and an app is provided for this adjusted model with high discrimination ability in internal validation (AUC = 0.833). Our clinical utility analysis guide us to choose 40% as the best threshold probability.Conclusions: We found risk and protective factors associated with PMPC, but our data were not conclusive to demonstrate its relation with maternal thrombophilia. However, the challenger finding is the clinical utility of antithrombotic drugs as a protective factors in PMPC prevention. It is possible to identify patients with high risk of PMPC through a combined predictive model, for counseling treatment.

Prediction of Large for Gestational Age by Ultrasound at 35 Weeks and Impact of Ultrasound-Delivery Interval: Comparison of 6 Standards.

OBJECTIVE: The aim of the study was to assess the predictive ability of the ultrasound estimated percentile weight (EPW) at 35 weeks to predict large for gestational age (LGA) at term delivery according to 6 growth standards, including population, population-customized, and international references. The secondary objectives were to determine its predictive ability to detect adverse perinatal outcomes (APOs) and whether the ultrasound-delivery interval influences the detection rate of LGA newborns. METHODS: This was a retrospective cohort study of 9,585 singleton pregnancies. Maternal clinical characteristics, fetal ultrasound data obtained at 35 weeks, and pregnancy and perinatal outcomes were used to calculate EPWs to predict LGAs at delivery according to the customized and the non-customized (NC) Miguel Servet University Hospital (MSUH), the customized Figueras, the NC Fetal Medicine Foundation (FMF), the NC INTERGROWTH-21st, and the NC World Health Organization (WHO) standards. RESULTS: For a 10% false-positive rate, detection rates for total LGAs at delivery ranged from 31.2% with the WHO (area under the curve [AUC] 0.77; 95% confidence interval [CI], 0.76-0.79) to 56.5% with the FMF standard (AUC 0.85; 95% CI, 0.84-0.86). Detection rates and values of AUCs to predict LGAs by ultrasound-delivery interval (range 1-6 weeks) show higher detection rates as the interval decreases. APO detection rates ranged from 2.5% with the WHO to 12.6% with the Figueras standard. CONCLUSION: The predictive ability of ultrasound estimated fetal weight at 35 weeks to detect LGA infants is significantly greater for FMF and MSUH NC standards. In contrast, the APO detection rate is significantly greater for customized standards. The shorter ultrasound-delivery interval relates to better prediction rates.

A cohort study of fetal growth in twin pregnancies by chorionicity: comparison with European and American standards.

OBJECTIVE: To develop fetal growth standards for twin gestations by placental chorionicity in a Spanish population and compare them with European and American standards to estimate the suitability of their use in clinical practice. STUDY DESIGN: This was a retrospective cohort study of 518 twin pregnancies, 435 dichorionic-diamniotic and 83 monochorionic-diamniotic, performed between January 2012 and December 2017. A total of 4,783 and 1,455 estimated fetal weights were considered from the 17th to the 37th week of gestation, using multilevel models, to build dichorionic-diamniotic and monochorionic-diamniotic standards, respectively. The percentages of small and large for gestational age were calculated as a model adjustment measure and adjustment to the studied data and the values provided by our model were compared against those of six European and American twin standards and three singleton standards. Correlation analyses between percentile predictions were performed using Cohen kappa coefficient. The predictive ability to detect small for gestational age was also provided by the sensitivity and positive predictive value. RESULTS: We found slight differences between standards by chorionicity, being dichorionic-diamniotic percentiles slightly higher than monochorionic-diamniotic ones from the 17th to 37th weeks' gestation. For dichorionic-diamniotic cases, both our standard (9.8-8.2) and that of Grantz (8.2-10.5) showed good adjustments for the 10th and 90th percentiles while the other compared standards underestimated or overestimated them. For monochorionic-diamniotic cases, both our standard (10.2-8.5) and that of Shivkumar (11.4-6.8) had the most suitable adjustment. The correlation analysis between small and large for gestational age cases provided by standards, showed clear differences among them. Kappa's coefficient showed a substantial agreement between both Ananth (0.7) and Stirrup (0.69) dichorionic-diamniotic cases and our standard. There was also a substantial agreement between the Shivkumar (0.77) standard and our results for monochorionic-diamniotic cases. The correlation was moderate for all other comparisons. CONCLUSIONS: Our model showed a good adjustment to the studied population. There are clear differences among small and large for gestational age cases provided by twin standards in our studied population. The twin growth standards depend on the population characteristics and model structure. We found the use of singleton standards for twin pregnancies inadequate.

Detection of Adverse Perinatal Outcomes at Term Delivery Using Ultrasound Estimated Percentile Weight at 35 Weeks of Gestation: Comparison of Five Fetal Growth Standards.

OBJECTIVE: To assess the predictive ability of the ultrasound estimated percentile weight (EPW) at 35 weeks of pregnancy to predict adverse perinatal outcomes (APOs) at term delivery according to 5 fetal growth standards, including population, population-customized, and international references. METHODS: This was a retrospective cohort study of 9,585 singleton pregnancies. Maternal clinical characteristics, fetal ultrasound data obtained at 35 weeks and pregnancy and perinatal outcomes were used to calculate EPWs to predict APOs according to: the customized and noncustomized (NC) Miguel Servet University Hospital (MSUH), the customized Figueras, the NC INTERGROWTH-21st, and the NC World Health Organization (WHO) international standards. APOs were defined as the occurrence of cesarean or instrumental delivery for nonreassuring fetal status, 5-min Apgar score < 7, arterial cord blood pH <7.10, or stillbirth. The predictive ability of EPW for APOs was analyzed using the area under the curve (AUC), and sensitivities were calculated for different false-positive rates (FPRs). RESULTS: For a 10% FPR, detection rates for total APOs ranged between 12.7% with the customized MSUH (AUC 0.52; 95% CI 0.50-0.55) and 14.4% with the NC MSUH standard (AUC 0.55; 95% CI 0.53-0.57) for EPW by ultrasound; and from 22.0% with the customized MSUH standard (AUC 0.60; 95% CI 0.58-0.63) to 27.8% with the NC WHO (AUC 0.65; 95% CI 0.63-0.68) for EPW at delivery. CONCLUSIONS: The predictive capacity of the EPW for APOS is limited and similar, by both ultrasound and at delivery, for the 5 growth standards, without significant differences between customized and NC standards.

Is it too late to quit? Premature placental aging and smoking / ¿Demasiado tarde para dejarlo? Envejecimiento placentario prematuro y tabaquismo

Objetive: The aim of this study was to determine the relationship between being smoker, ex-smoker or passive smoker and the risk of developing a placental Grannum-grade- III and the interval of time necessary between stop smoking and becomepregnant. Material and methods: A retrospective case-control analysis was performed for women with singleton pregnancy that had ultrasound in the third trimester, establishing two groups according to the classification of placental aging purposed by Grannum: Grannum grade III and Grannum grade I-II. In both groups,maternal characteristics and perinatal outcomes were studied, and also the risk of placental premature aging in pregnant smokers, ex-smokers and passive smokers. Results: Being smokers (p < 0.01) or suffer from a hypertensive disorder (p = 0.01)is associated with a higher risk of premature placental aging. Smokers who quit during early pregnancy and passive smokers behave like smokers,withno significant differences between them. We have also studied ex-smokers and the period of time between quit and became pregnant and the findings show a decrease of six times risk of to develop a Grannum grade III placenta for women who retires from smoke more than a year beforepregnancy. Conclusions: The main causes of developing a premature aging of the placenta are being a smoker and developing a hypertensive disorder. Smoking cessation should occur before becoming pregnant, ideally more than 18 months earlier, in order to avoid placentalabnormalities

Objetivo: el objetivo de este estudio fue determinar la relación entre ser fumadora, exfumadora o fumadora pasiva y el riesgo de desarrollar una placenta Grannum grado III y el intervalo de tiempo necesario entre dejar de fumar y quedarembarazada. Material y métodos: se realizó un análisis retrospectivo de casos y controles para mujeres con embarazos únicos a las que se realizó una ecografía en el tercer trimestre, estableciendo dos grupos según la clasificación de envejecimiento placentario propuesta por Grannum: Grannum grado III y Grannum grado I-II. En ambos grupos, se estudiaron las características maternas y los resultados perinatales, así como el riesgo de envejecimiento prematuro de placenta en mujeres embarazadas fumadoras, exfumadoras y fumadoraspasivas. Resultados: ser fumador (p < 0,01) o padecer un trastorno hipertensivo (p = 0,01) se asocia con mayor riesgo de envejecimiento prematuro de placenta. Las fumadoras que abandonan el hábito al inicio del embarazo y las fumadoras pasivas se comportan como fumadoras, sin diferencias significativas entre ellas. Asimismo se ha estudiado a las exfumadoras y el período de tiempo entre dejar de fumar y quedar embarazada y los hallazgos muestran un riesgo seis veces menor de desarrollar una placenta de grado III de Grannum en mujeres que abandonaron el hábito más de doce meses antes de quedar gestantes. Conclusiones: las principales causas de desarrollar envejecimiento prematuro de placenta son ser fumadora y desarrollar un trastorno hipertensivo. El abandono del hábito tabáquico debe producirse antes de quedar embarazada, idealmente más de 18 meses antes, para evitar anormalidades placentarias

Maternal morbidity after implementation of a postpartum hemorrhage protocol including use of misoprostol.

OBJECTIVE: To compare maternal morbidity before and after implementation of a postpartum hemorrhage (PPH) protocol that included misoprostol. METHODS: A retrospective analysis was performed using data from 34 631 deliveries recorded at a Spanish hospital between January 1, 2007, and December 31, 2014. The PPH protocol was implemented in 2009 and included use of misoprostol and the Bakri balloon. RESULTS: The pre-implementation and post-implementation groups comprised 9394 and 25 237 women, respectively. Women in the pre-implementation group tended to have lower hemoglobin levels than did those in the post-implementation group: 811 (8.6%) versus 1349 (5.3%) for levels less than 90 g/L, and 272 (2.9%) versus 497 (2.0%) for levels less than 80 g/L (both P<0.001). Implementation of the PPH protocol was also associated with a decrease in the frequency of postpartum hysterectomies owing to uterine atony (0.11 cases per 1000 deliveries vs 0.53 cases per 1000 deliveries for the pre-implementation group; P=0.063). Pregnancy length, maternal age, neonatal weight at delivery, multiple pregnancy, previous cesarean delivery, parity, operative vaginal delivery, induced labor, cesarean delivery, and not using the PPH protocol were found to predict postpartum anemia in the multivariate analysis (all P<0.001). CONCLUSION: Implementation of the PPH protocol decreased rates of postpartum anemia and postpartum hysterectomy owing to uterine atony.

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy.

BACKGROUND: PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups. METHODS: Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference's nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves. RESULTS: We detect 28% of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20% at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95%:0.68-0.79) and 0.786 for HGPCa (C.I.95%:0.71-0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40% could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31% for the threshold probability of 40%. CONCLUSIONS: PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40% should be counseled to undergo an IBx if opportunistic screening is required.

Three linked nomograms for predicting biochemical failure in prostate cancer treated with radiotherapy plus androgen deprivation therapy.

BACKGROUND: Nomograms were established to predict biochemical recurrence (BCR) after radiotherapy (RT) with a low weight of the characteristic variables of RT and androgen deprivation therapy (ADT). Our aim is to provide a new stratified tool for predicting BCR at 4 and 7 years in patients treated using RT with radical intent. MATERIALS AND METHODS: A retrospective, nonrandomized analysis was performed on 5044 prostate cancer (PCa) patients with median age 70 years, who received RT-with or without ADT-between November 1992 and May 2007. Median follow-up was 5.5 years. BCR was defined as a rise in serum prostate-specific antigen (PSA) of 2 ng/ml over the post-treatment PSA nadir. Univariate association between predictor variables and BCR was assessed by the log-rank test, and three linked nomograms were created for multivariate prognosis of BCR-free survival. Each nomogram corresponds to a category of the Gleason score-either 6,7, or 8-10-and all of them were created from a single proportional hazards regression model stratified also by months of ADT (0, 1-6, 7-12, 13-24, 25-36, 36-60). The performance of this model was analyzed by calibration, discrimination, and clinical utility. RESULTS: Initial PSA, clinical stage, and RT dose were significant variables (p < 0.01). The model showed a good calibration. The concordance probability was 0.779, improving those obtained with other nomograms (0.587, 0.571, 0.554) in the database. Survival curves showed best clinical utility in a comparison with National Comprehensive Cancer Network (NCCN) risk groups. CONCLUSION: For each Gleason score category, the nomogram provides information on the benefit of adding ADT to a specific RT dose.

[Usefulness of clinical nomograms and predictive models for pca. Predictive clinical factors of tumor agressiveness]. / Utilidad de los nomogramas clínicos y modelos predictivos del cáncer de próstata. Factores clínicos de predicción de agresividad tumoral.

In this narrative review we present the natural evolution of predictive models to their presentation in the nomogram format. We show their clinical usefulness and the objective parameters that contribute to their clinical use: calibration, discrimination, decision curves and probability density functions. We continue detailing the various existing predictive models/nomograms in relation to prostate cancer aggressiveness before and after biopsy, before and after primary treatment, recurrence and castration resistance. Finally we include future markers in advanced stage of implementation in the context of nomograms and related to the aggressiveness of prostate cancer: PCA3, PHI coefficient, 4Kscore, cell cycle progression (Prolaris®) and single nucleotid polymorphisms.