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OBJECTIVE: To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer. MATERIAL AND METHODS: Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed. RESULTS: 250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (P=0.0084). CONCLUSION: In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy.

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INTRODUCTION: The authors aimed to: (1) determine how length of stay (LOS) and complication rates changed over the past 10 years, in comparison to values estimated by the ACS-NSQIP surgical risk calculator, at a single private institution open to external surgeons; and (2) determine preoperative patient factors associated with complications. METHODS: We retrospectively assessed 1018 consecutive patients who underwent primary elective THA over 10 years. We excluded 87 with tumours and 52 with incomplete records. Clinical data of the remaining 879 were used to determine real LOS and rate of 9 adverse events over time, as well as to estimate these values using the risk calculator. Its predictive reliability was represented on receiver operating characteristic curves. Multivariable analyses were performed to determine associations of complications with age, sex, ASA score, diabetes, hypertension, heart disease, smoking and BMI. RESULTS: Over the 10-year period, real LOS and real complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time. The overall estimated and real rates of any complication were respectively 3.3% and 2.8%. The risk calculator had fair reliability for predicting any complications (AUC 0.72). Overall estimated LOS was shorter than the real LOS in 764 (86.9%) patients. Multivariable analysis revealed risks of any complication to be greater in patients aged ⩾75 (OR = 4.36, p = 0.002), and with hypertension (OR = 3.13, p = 0.016). CONCLUSIONS: Since the implementation of clinical pathways at our institution, real LOS and complication rates decreased considerably, while LOS and complication rates estimated by the surgical risk calculator had little or no change. The difference between real and estimated LOS decreased over time, which could lead some clinicians to reconsider their discharge criteria, knowing that advanced age and hypertension increased risks of encountering complications.

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OBJECTIVE: Multiple models have tried to predict the morbidity and mortality of liver resections (HR). This study aims to determine the efficacy and validity of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator in a cohort of patients undergoing HR in Veracruz, Mexico. MATERIAL AND METHODS: Retrospective analysis of patients undergoing HR between 2005 and 2019. To estimate the performance of the calculator, the observed results were compared with the average risk predicted by the calculator, using ROC curve, Brier score and Z test. RESULTS: 67 patients were evaluated, mean age 51.9 years of age, 50.7% female. The majority of resections (56.7%) were for malignancy and 62.1% were partial hepatectomies (up to 3 liver segments). The morbidity was 25.4% and the mortality 4.5%. There was a good prediction in the complications (serious complication: C = 0.725 statistic, Brier score 0.26, p = 0.006 and any statistical complication C = 0.731, Brier score 0.33, p = 0.005) and mortality (C = 0.922 statistic, Brier score 0.005, p = 0.014). CONCLUSIONS: The application of the ACS-NSQIP calculator in patients undergoing HR has good discrimination power and good predictive ability. Prediction of postoperative risks achieves a preoperative planning of the appropriate procedure.

OBJETIVO: Múltiples modelos han intentado predecir la morbilidad y mortalidad de las resecciones hepáticas (RH). Este estudio tiene por objetivo determinar la eficacia y validez de la calculadora de riesgo quirúrgico del American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) en una cohorte de pacientes sometidos a RH en Veracruz, México. MATERIAL Y MÉTODOS: Análisis retrospectivo de pacientes sometidos a RH entre 2005 y 2019. Se compararon los resultados observados con la media del riesgo previsto por la calculadora, mediante Curva ROC, score de Brier y prueba Z. RESULTADOS: Se evaluaron 67 pacientes, con 51.9 años de edad media, un 50.7% de sexo femenino. La mayoría de las resecciones (56.7%) fueron por patología maligna y el 62.1% fueron hepatectomías parciales. La morbilidad fue del 25.4% y la mortalidad del 4.5%. Hubo una predicción buena en las complicaciones (complicación seria, estadística: C: 0.725, score Brier: 0.26, p = 0.006 y cualquier complicación, estadística: C: 0.731, score Brier: 0.33, p = 0.005) y la mortalidad (estadística C: 0.922, score Brier: 0.005, p = 0.014). CONCLUSIONES: La aplicación de la calculadora ACS-NSQIP en pacientes sometidos a RH tiene buen poder de discriminación y buena habilidad predictiva. Predecir riesgos postoperatorios logra una adecuada planeación preoperatoria del procedimiento.

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BACKGROUND: Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients' individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources. OBJECTIVE: The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models' overall performance. METHODS: We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model's overall accuracy. RESULTS: We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history. CONCLUSIONS: Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population's sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented. TRIAL REGISTRATION: PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110.

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The burden of adolescent depression is high in low- and middle-income countries (LMICs), yet research into prevention is lacking. Development and validation of models to predict individualized risk of depression among adolescents in LMICs is rare but crucial to ensure appropriate targeting of preventive interventions. We assessed the ability of a model developed in Brazil, a middle-income country, to predict depression in an existing culturally different adolescent cohort from Nepal, a low-income country with a large youth population with high rates of depression. Data were utilized from the longitudinal study of 258 former child soldiers matched with 258 war-affected civilian adolescents in Nepal. Prediction modelling techniques were employed to predict individualized risk of depression at age 18 or older in the Nepali cohort using a penalized logistic regression model. Following a priori exclusions for prior depression and age, 55 child soldiers and 71 war-affected civilians were included in the final analysis. The model was well calibrated, had good overall performance, and achieved good discrimination between depressed and non-depressed individuals with an area under the curve (AUC) of 0.73 (bootstrap-corrected 95% confidence interval 0.62-0.83). The Brazilian model comprising seven matching sociodemographic predictors, was able to stratify individualized risk of depression in a Nepali adolescent cohort. Further testing of the model's performance in larger socio-culturally diverse samples in other geographical regions should be attempted to test the model's wider generalizability.

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BACKGROUND: Predictive tools are useful adjuncts in surgical planning. They help guide patient selection, candidacy for inpatient vs outpatient surgery, and discharge disposition as well as predict the probability of readmissions and complications after total joint arthroplasty (TJA). Surgeons may find it difficult due to significant variation among risk calculators to decide which tool is best suited for a specific patient for optimal decision-based care. Our aim is to perform a systematic review of the literature to determine the existing post-TJA readmission calculators and compare the specific elements that comprise their formula. Second, we intend to evaluate the pros and cons of each calculator. METHODS: Using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols protocol, we conducted a systematic search through 3 major databases for publications addressing TJA risk stratification tools for readmission, discharge disposition, and early complications. We excluded those manuscripts that were not comprehensive for hips and knees, did not list discharge, readmission or complication as the primary outcome, or were published outside the North America. RESULTS: Ten publications met our criteria and were compared on their sourced data, variable types, and overall algorithm quality. Seven of these were generated with single institution data and 3 from large administrative datasets. Three tools determined readmission risk, 5 calculated discharge disposition, and 2 predicted early complications. Only 4 prediction tools were validated by external studies. Seven studies utilized preoperative data points in their risk equations while 3 utilized intraoperative or postsurgical data to delineate risk. CONCLUSION: The extensive variation among TJA risk calculators underscores the need for tools with more individualized stratification capabilities and verification. The transition to outpatient and same-day discharge TJA may preclude or change the need for many of these calculators. Further studies are needed to develop more streamlined risk calculator tools that predict readmission and surgical complications.

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The cardiovascular diseases (CVDs) have a growing impact over the world mortality, affecting mostly low and middle-income countries. This is due to changes in the population pyramid and the increase in unhealthy lifestyles that predispose the global population to cardiovascular risk factors such as overweight, obesity, smoking, hypertension, diabetes, dyslipidemias and metabolic syndrome. Ischemic heart disease and the cerebral vascular event remain the first causes of death reported by the World Health Organization (WHO) for more than a decade. Mexico has high prevalence in obesity, overweight, hypertension and diabetes in the population over 20 years old; Within the OECD countries (Organization for Economic Cooperation and Development) are the country with the highest mortality due to acute myocardial infarction over 45 years in the first 30 days. In order to face the growing pandemic of CVDs, the IMSS, it has developed and implemented a comprehensive care program called "A Todo Corazon", it is the first program of integral care which seeks to strengthen the actions to improving the impact of CVDs from health. This review is focused on describing the 7 axes that make up the program; each axe is described in detail. Axes one to three are dedicated to promotion and primary prevention of CVDs. Axes 4 and 5 are dedicated to infarction code, as a national strategy to confront the principal cause of death in Mexico. Finally axes 6 and 7 are dedicated to intensive care, secondary prevention and rehabilitation of CVDs.

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OBJECTIVE: To assess the usefulness of a bronchopulmonary dysplasia (BPD) outcome estimator developed by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) in identifying high-risk preterm infants treated with steroids. STUDY DESIGN: This was a single-center retrospective study of infants born ≤30 weeks of gestational age. The NICHD BPD outcome estimator was used to retrospectively calculate BPD risk at various postnatal ages. The best combination of risk estimates for identifying steroid treatment was identified using stepwise model selection. A cut-off value with the best combination of sensitivity and specificity was identified using receiver operating characteristic analysis. RESULTS: A total of 165 infants born preterm (mean gestational age 26 ± 1.6 weeks, mean birth weight 837 ± 171 g) were included. Of these, 61 were treated with steroids for BPD and 104 were not. Risk estimates for BPD or death were significantly greater in infants treated with steroids compared with controls. Both combined risk for severe BPD or death and single risk of no BPD were identified as factors with the best predictive power for identifying treatment with steroids, with accurate prediction possible as early as the second week of life. A greater than 37% risk for severe BPD or death or a less than 3% risk of no BPD on day of life 14 had 84%-92% sensitivity and 77%-80% specificity for predicting steroid treatment. CONCLUSION: The NICHD BPD outcome estimator can be a useful objective tool for identifying infants at high risk for BPD who may benefit from postnatal steroids.

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OBJECTIVES: To evaluate factors affecting the risk of prostate cancer (CaP) and high-grade disease (HGCaP, Gleason score ≥ 7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC). METHODS AND MATERIALS: From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of CaP, and history of a prior prostate biopsy on CaP and HGCaP, separately. Internal discrimination, goodness-of-fit, and clinical utility of the resulting models were assessed with comparison to the PCPTRC. RESULTS: Rates of both CaP (73.2%) and HGCaP (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of CaP but overestimated the risk of HGCaP. Four factors influencing CaP on biopsy were logPSA, DRE, family history and a prior biopsy history (all P < 0.001). The internal AUC of the logistic model was 0.823 compared with 0.785 of the PCPTRC for CaP (P < 0.001). The same 4 factors were significantly associated with HGCaP as well and the AUC was 0.779 compared with 0.766 of the PCPTRC for HGCaP (P = 0.13). CONCLUSIONS: Lack of screening programs or regular urologic checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics.

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