GASTO ENERGÉTICO EN REPOSO ESTIMADO MEDIANTE SEIS FÓRMULAS PREDICTIVAS Y SU COMPARACIÓN CON LA BIOIMPEDANCIA EN RESIDENTES DE TRES CIUDADES DEL PERÚ

Objetivo: Determinar el gasto energético en reposo (GER) estimado mediante seis fórmulas predictivas comparativamente con la bioimpedancia en una muestra de peruanos. Material y métodos: Estudio transversal analítico comparativo, con análisis de base de datos secundario de la cohorte CRONICAS. La variable principal fue el GER determinado por la bioimpedancia. Las otras variables de importancia son las 6 ecuaciones predictivas para estimar el GER, las cuales fueron Harris-Benedict (HB), Mifflin-St Jeor (Mjeor), Organización de las Naciones Unidas para la Agricultura y la Alimentación y la Organización Mundial de la Salud (OMS), "Institute of Medicine" (IOM), Estimación Rápida y Valencia. Resultados: se trabajó con un total de 666 sujetos. Las ecuaciones predictivas mostraron una correlación positiva con el GER determinado por la bioimpedancia; la ecuación con mayor correlación fue la Mjeor, tuvo una correlación muy fuerte de 0.95. En el análisis multivariado se observó que la ecuación Mjeor fue la que menos sobrestimo el GER, aumenta 0.77 Kcal/día (0.769-0.814; p<0,001) de acuerdo a cada punto que aumentaba el GER determinado por bioimpedancia. La fuerza de asociación entre el Mjeor y la bioimpedancia fue 0.9037. Además, en la regresión de los datos (peso, talla, edad) en la ecuación de Mjeor se observó que los coeficientes obtenidos fueron los mismos a los usados en la ecuación original. Conclusión: la ecuación Mjeor mostró sería la más adecuada para estimar el GER en población peruana. Futuros estudios prospectivos deben confirmar la utilidad de esta fórmula con potencial utilidad en atención primaria de la salud

Analysis of statistical knowledge of Peruvian medical students: a cross-sectional analytical study based on a survey

Introduction: Despite the growing awareness of the importance of knowledge in biostatistics, many investigations worldwide have found that medical students have a poor understanding of it. Objective: To determine the percentage of Peruvian medical students with sufficient biostatistics knowledge and the associated factors. Methods: Cross-sectional analytical study. Application of a virtual survey to medical students from different faculties in Peru. Results: 56.46% of medical students have insufficient knowledge of biostatistics. A statistically significant association was found for those who were 25 years of age or older (aPR: 1.195; 95% CI 1.045 - 1.366; p=0.009); being between the 9th and 12th semester (aPR: 1.177; 95% CI 1.001 - 1.378; p=0.037) and medical internship (aPR: 1.373; 95% CI 1.104 - 1.707; p=0.004); take an external course in biostatistics, epidemiology or research (aPR: 4.016; 95% CI 3.438 - 4.693; p<0.001); having read more than 12 articles per year (aPR: 1.590; 95% CI 1.313 - 1.967; p<0.001); and publish at least one scientific article (aPR: 1.549; 95% CI 1.321 - 1.816; p<0.001) or more than one (PR: 2.312; 95% CI 1.832 - 2.919; p<0.001). Conclusions: There is insufficient knowledge of biostatistics in medical students. The factors associated with a good understanding of this were age, academic semester, the number of articles read and published, and having taken an external course.Campus Lima Nort

Analysis of Statistical Knowledge of Peruvian Medical Students: A Cross-Sectional Analytical Study Based on a Survey

Introduction: Despite the growing awareness of the importance of knowledge in biostatistics, many investigations worldwide have found that medical students have a poor understanding of it. Objective: To determine the percentage of Peruvian medical students with sufficient biostatistics knowledge and the associated factors. Methods: Cross-sectional analytical study. Application of a virtual survey to medical students from different faculties in Peru. Results: 56.46% of medical students have insufficient knowledge of biostatistics. A statistically significant association was found for those who were 25 years of age or older (aPR: 1.195; 95% CI 1.045 - 1.366; p=0.009); being between the 9th and 12th semester (aPR: 1.177; 95% CI 1.001 - 1.378; p=0.037) and medical internship (aPR: 1.373; 95% CI 1.104 - 1.707; p=0.004); take an external course in biostatistics, epidemiology or research (aPR: 4.016; 95% CI 3.438 - 4.693; p&lt;0.001); having read more than 12 articles per year (aPR: 1.590; 95% CI 1.313 - 1.967; p&lt;0.001); and publish at least one scientific article (aPR: 1.549; 95% CI 1.321 - 1.816; p&lt;0.001) or more than one (PR: 2.312; 95% CI 1.832 - 2.919; p&lt;0.001). Conclusions: There is insufficient knowledge of biostatistics in medical students. The factors associated with a good understanding of this were age, academic semester, the number of articles read and published, and having taken an external course

Niveles elevados de fibrinógeno en el sujeto delgado metabólicamente obeso: un estudio transversal analítico a partir de una muestra de pobladores peruanos: Niveles de fibrinógeno en sujetos delgados metabólicamente obesos

Introduction: Plasmatic fibrinogen is known as a main risk factor to cardiometabolic diseases through mechanisms such as thrombin formation, platelet aggregation and inflammation, part of the endothelial dysfunction leading to Acute Coronary Syndrome. Elevated fibrinogen levels may be present in subjects with obesity; however, there is no enough information on whether this is also the same among metabolically obese normal-weight subjects (MONW). Objective: To determine the association between being MONW and fibrinogen levels in a sample of Peruvian inhabitants. Methodology: Cross-sectional analytical study. Secondary data analysis of the PERU MIGRANT study (2007, 989 participants). For the diagnosis of MONW, it was considered if it presented two or more characteristics such as: waist circumference (anthropometric evaluation), triglycerides, fasting glucose, systolic blood pressure or diastolic blood pressure, HDL-cholesterol, insulin resistance through HOMA-IR, C-reactive protein. Elevated fibrinogen ≥ 450 mg/dl was considered. For regression analysis we used generalized linear model with link log and family Poisson with robust variance. We present crude prevalence ratio and adjusted by mentioned variables, as association parameter. We considered confidence interval 95%. Results: Of the 393 selected participants, 46.3% were women, the median of age was 47(37-56), only 13.5% had elevated fibrinogen levels. The prevalence of the MONW subject was 32.67%. In the correlation analysis, there was only a statistically significant relationship between fibrinogen and plasma CRP (rho= 0.54; p&lt;0.001). In multiple regression, association was found between being MONW and high plasma fibrinogen levels (PR=1.93, 95% CI: 1.44-2.57; p&lt;0.001). Conclusion: There is an association between high levels of plasmatic fibrinogen and MONW. These results can serve as a first step for future prospective research, either to be a risk factor or as an additional marker of consideration for monitoring and diagnosis in normal-weight people.Introducción: El fibrinógeno plasmático, es reconocido como un factor de riesgo importante para eventos cardiometabólicos a través de mecanismos como la formación de trombina, agregación plaquetaria e inflamación, todos partes de la disfunción endotelial conducentes al Síndrome Coronario Agudo. Elevados niveles pueden estar presentes en sujetos con obesidad, sin embargo, no hay información suficiente sobre si esto también es igual en los sujetos delgados metabólicamente obesos (DMO). Objetivo: Determinar la asociación entre el DMO y niveles de fibrinógeno en una muestra de pobladores peruanos. Metodología: Estudio transversal analítico. Análisis de datos secundarios del estudio PERU MIGRANT (2007, 989 participantes). Para el diagnóstico de DMO se consideró si presentaba dos o más características de siete criterios metabólicos: circunferencia de la cintura (mediante evaluación antropométrica), triglicéridos, glucosa en ayunas, presión arterial sistólica y diastólica, colesterol-HDL, HOMA-IR, proteína C reactiva (PCR). Se consideró fibrinógeno (elevado ≥ 450 mg/dl). Para el análisis de regresión, se realizó un modelo lineal generalizado con enlace log y familia Poisson con varianza robusta. De esa forma, se obtuvo como medida de asociación las razones de prevalencia crudas (RPc) y ajustadas (RPa) por las covariables mencionadas, se consideró intervalos de confianza al 95% (IC 95%). Resultados: De los 393 participantes seleccionados, el 46,3% fueron mujeres, la mediana de edad fue 47(37–56), 13,5% presentó niveles de fibrinógeno elevado. La prevalencia de DMO fue 32,6%. Solo hubo una relación estadísticamente significativa entre fibrinógeno y el PCR-plasmático (rho= 0,54; p&lt;0,001). La regresión múltiple, encontró asociación entre el DMO y el nivel altos fibrinógeno plasmático (RP=1,93 IC95%: 1,44–2,57; p&lt;0,001). Conclusión: Existe asociación entre los niveles altos de fibrinógeno plasmático y el DMO. Estos resultados pueden servir para futuras investigaciones prospectivas, ya sea para considerarlo un factor de riesgo o como un marcador adicional para el seguimiento y diagnóstico en personas delgadas

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research