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Objective@#To investigate the factors related to severe acute pancreatitis (SAP) with intestine functional disturbance (IFD) and to establish the multiple predictor models of SAP with IFD.@*Methods@#Clinical data of consecutive SAP patients admitted to department of gastroenterology of Beijing Shijitan Hospital, Capital Medical University from January 2015 to March 2019 were retrospectively collected and analyzed. According to the occurrence of IFD at 48 hours after onset, the patients were divided into IFD group and control group. The clinical indicators within 4 hours after admission were compared between the two groups, and the independent predictive factors for SAP with IFD were screened by single factor analysis and multiple classified Logistic regression analysis. The unweighted predictive score (unwScore) and weighted predictive score (wScore) models were constructed by combining the independent predictors. The receiver operating characteristic (ROC) curves of SAP patients with IFD were plotted by independent predictive factors and predictive models, and the clinical predictive effect of each independent predictive index and predictive models were analyzed.@*Results@#A total of 149 patients with SAP were enrolled, including 87 males and 62 females, with age of (52.8±18.1) years old. There were 45 patients in IFD group and 104 patients in control group.Univariate analysis and multiple classified Logistic regression analysis showed that high sensitive C-reactive protein (hs-CRP), blood urea nitrogen (BUN), serum creatinine (SCr), serum calcium (Ca), procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR) were independent predictive factors of SAP with IFD. The ROC curve was used to calculate the cut-off value of the above indexes to predict IFD, and unwScore model was established. The cut-off score of IFD prediction by the unwScore model was 3 points, and the probability of IFD increased with the increase of the score. The area under ROC curve (AUC) of unwScore was 0.944, the sensitivity was 95.6%, the specificity was 94.2%, the positive predictive value (PPV) was 87.8%, and the negative predictive value (NPV) was 98.0%. The binary Logistic regression analysis of hs-CRP, BUN, Ca, SCr, PCT and NLR were carried out, and wScore model was established. The AUC of wScore was 0.959, the sensitivity was 95.9%, the specificity was 96.2%, the PPV was 91.5%, and the NPV was 98.1%; predictive value was superior to each independent index and unwScore model.@*Conclusions@#hs-CRP, BUN, SCr, Ca, PCT and NLR were independent predictive factors of SAP with IFD. The multiple predictor models of SAP with IFD have a good predictive efficiency which may provide valuable clinical reference for prediction and treatment.
ABSTRACT
Objective To investigate the factors related to severe acute pancreatitis (SAP) with intestine functional disturbance (IFD) and to establish the multiple predictor models of SAP with IFD. Methods Clinical data of consecutive SAP patients admitted to department of gastroenterology of Beijing Shijitan Hospital, Capital Medical University from January 2015 to March 2019 were retrospectively collected and analyzed. According to the occurrence of IFD at 48 hours after onset, the patients were divided into IFD group and control group. The clinical indicators within 4 hours after admission were compared between the two groups, and the independent predictive factors for SAP with IFD were screened by single factor analysis and multiple classified Logistic regression analysis. The unweighted predictive score (unwScore) and weighted predictive score (wScore) models were constructed by combining the independent predictors. The receiver operating characteristic (ROC) curves of SAP patients with IFD were plotted by independent predictive factors and predictive models, and the clinical predictive effect of each independent predictive index and predictive models were analyzed. Results A total of 149 patients with SAP were enrolled, including 87 males and 62 females, with age of (52.8±18.1) years old. There were 45 patients in IFD group and 104 patients in control group. Univariate analysis and multiple classified Logistic regression analysis showed that high sensitive C-reactive protein (hs-CRP), blood urea nitrogen (BUN), serum creatinine (SCr), serum calcium (Ca), procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR) were independent predictive factors of SAP with IFD. The ROC curve was used to calculate the cut-off value of the above indexes to predict IFD, and unwScore model was established. The cut-off score of IFD prediction by the unwScore model was 3 points, and the probability of IFD increased with the increase of the score. The area under ROC curve (AUC) of unwScore was 0.944, the sensitivity was 95.6%, the specificity was 94.2%, the positive predictive value (PPV) was 87.8%, and the negative predictive value (NPV) was 98.0%. The binary Logistic regression analysis of hs-CRP, BUN, Ca, SCr, PCT and NLR were carried out, and wScore model was established. The AUC of wScore was 0.959, the sensitivity was 95.9%, the specificity was 96.2%, the PPV was 91.5%, and the NPV was 98.1%;predictive value was superior to each independent index and unwScore model. Conclusions hs-CRP, BUN, SCr, Ca, PCT and NLR were independent predictive factors of SAP with IFD. The multiple predictor models of SAP with IFD have a good predictive efficiency which may provide valuable clinical reference for prediction and treatment.
ABSTRACT
Objective To investigate the factors related to severe acute pancreatitis (SAP) with intestine functional disturbance (IFD) and to establish the multiple predictor models of SAP with IFD. Methods Clinical data of consecutive SAP patients admitted to department of gastroenterology of Beijing Shijitan Hospital, Capital Medical University from January 2015 to March 2019 were retrospectively collected and analyzed. According to the occurrence of IFD at 48 hours after onset, the patients were divided into IFD group and control group. The clinical indicators within 4 hours after admission were compared between the two groups, and the independent predictive factors for SAP with IFD were screened by single factor analysis and multiple classified Logistic regression analysis. The unweighted predictive score (unwScore) and weighted predictive score (wScore) models were constructed by combining the independent predictors. The receiver operating characteristic (ROC) curves of SAP patients with IFD were plotted by independent predictive factors and predictive models, and the clinical predictive effect of each independent predictive index and predictive models were analyzed. Results A total of 149 patients with SAP were enrolled, including 87 males and 62 females, with age of (52.8±18.1) years old. There were 45 patients in IFD group and 104 patients in control group. Univariate analysis and multiple classified Logistic regression analysis showed that high sensitive C-reactive protein (hs-CRP), blood urea nitrogen (BUN), serum creatinine (SCr), serum calcium (Ca), procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR) were independent predictive factors of SAP with IFD. The ROC curve was used to calculate the cut-off value of the above indexes to predict IFD, and unwScore model was established. The cut-off score of IFD prediction by the unwScore model was 3 points, and the probability of IFD increased with the increase of the score. The area under ROC curve (AUC) of unwScore was 0.944, the sensitivity was 95.6%, the specificity was 94.2%, the positive predictive value (PPV) was 87.8%, and the negative predictive value (NPV) was 98.0%. The binary Logistic regression analysis of hs-CRP, BUN, Ca, SCr, PCT and NLR were carried out, and wScore model was established. The AUC of wScore was 0.959, the sensitivity was 95.9%, the specificity was 96.2%, the PPV was 91.5%, and the NPV was 98.1%;predictive value was superior to each independent index and unwScore model. Conclusions hs-CRP, BUN, SCr, Ca, PCT and NLR were independent predictive factors of SAP with IFD. The multiple predictor models of SAP with IFD have a good predictive efficiency which may provide valuable clinical reference for prediction and treatment.
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Resumen OBJETIVO: desarrollar un modelo de predicción para conseguir un recién nacido vivo con el menor número de ovocitos capturados. MATERIALES Y MÉTODOS: estudio observacional, longitudinal y retrolectivo, efectuado en el Instituto Nacional de Perinatología entre 2011 y 2016 en ciclos de FIV en fresco. Criterios de inclusión: pacientes mayores de 18 años de edad, con diagnóstico de infertilidad, a quienes se realizó fertilización in vitro con transferencia de embriones en fresco (FIV-TE). Las variables de estudio fueron: edad, IMC, concentración basal de FSH, tipo de infertilidad, tiempo de infertilidad y número de ovocitos capturados. Se elaboró un árbol de decisión tipo CHAID y un modelo binario de regresión logística. Para el análisis estadístico se utilizó el programa Statistic Package for Social Sciences (SPSS). Se consideró significativa la probabilidad de error alfa < 5%. RESULTADOS: se registraron 673 ciclos, de los que se obtuvieron 5,910 óvulos. El número óptimo de ovocitos recuperados fue mayor de 12 (independientemente de la edad), con RM = 4.666, IC95%: 2.676-8.137, p = <0.01. Las mujeres menores de 37 años de edad, con concentración basal de FSH <4.2 mUI/mL y recuperación de hasta 5 ovocitos tuvieron mayor posibilidad (28%) de obtener un recién nacido vivo (χ2 = 7.797; gl = 1, p = <0.047); por su parte, las pacientes entre 38 y 40 años de edad (RM = 0.338, IC95%: 0.147-0.776, p = <0.011) y tiempo de infertilidad de 10 a 12 años de evolución (RM = 0.394, IC95%: 0.181-0.858, p = 0.019) tuvieron menor posibilidad de obtener un recién nacido vivo. CONCLUSION: el número óptimo de ovocitos a recuperar es mayor de 12 (independientemente de la edad). Las mujeres menores de 37 años de edad, con concentración basal de FSH <4.2 mUI/mL y captura de hasta 5 ovocitos tienen mayor posibilidad de tener un recién nacido vivo.
Abstract OBJECTIVE: Develop a model to optimize the reproductive outcome (live birth rate). Identify the minimal number of oocytes to capture. MAERIALS AND METHODS: Observational, longitudinal, and retrolective study was made. In fresh IVF cycles, performed at INPer between 2011-2016. A logistic regression model was fitted with a CHAID, and performed a decision tree to predict live birth (LBR). Inclusion criteria: patients over 18 years of age, diagnosed with infertility, who underwent in vitro fertilization with fresh embryo transfer (FIV-TE). The study variables were: age, BMI, basal FSH concentration, type of infertility, time of infertility and number of oocytes captured. A decision tree type CHAID and a binary logistic regression model were performed. Statistical Package for Social Sciences (SPSS) was used for the statistical analysis. The probability of error alpha <5% was considered significant. RESULTS: A total of 673 cycles were studied. The optimal number was >12 oocytes (OR = 4.666, 95% CI: 2.676-8.137, p=<0.01). The highest chance to have LB (28%), was in women <37 years old, with FSH <4.2 mIU / mL and <5 oocytes; χ2 = 7.797 (df = 1, p = <0.047). The lowest chance was in 38-40 years (OR = 0.338, 95% CI: 0.147-0.776, p = <0.011) with a longer lapse of infertility; 10-12 years (OR = 0.394, 95% CI: 0.181-0.858, p = 0.019). CONCLUSION: Our data suggest that in the >12 oocytes may be the optimal number to obtain, independent of the age. On the other hand the best chance to have a live birth is with an age <37, FSH <4.2 mIU/mL and <5 oocytes. Fewer oocytes than previously deemed optimal, because the probability of having a euploid embryo in this group of people is much bigger.
ABSTRACT
Cancer is one of the leading causes of morbidity and mortality worldwide. There are various detrimental symptoms experienced by a cancer patient due to the disease and the undergoing treatment which adversely affects the Quality of Life (QOL) in these patients. Therefore, QOL and its evaluation have turned out to be progressively vital in the health care system. Hence, the aim of our study was to develop a predictor model to predict the QOL in cancer patients receiving chemotherapy. The study was carried out in the Department of Radiotherapy and Oncology, Kasturba hospital, Manipal, a tertiary care hospital. Predictor model was developed to predict the Quality of Life Scores (QOLS) using multivariate regression analysis. A total of 387 patients participated in the study. Mean age of the patients was 50.85 ± 11.82 years (95% CI, 49.66-52.03). In our study, 16.54% had poor global health status/QOL, 72.35% had average and 11.11% had a high global health status/QOL. A significant difference was found in the QOLS based on the age group, site of cancer, drugs used in treatment of cancer, age as a predisposing factor and organ system affected due to ADRs (respiratory system, sensory system, skin and appendages). In the predictor model, the Coefficient of determination R-square (R2) was found to be 0.3267 indicating that 32.67% of the variation in the ‘quality of life score’ is explained by the independent variables included in the model. The F (45, 341) = 3.67, p < 0.001 indicating the overall significance of the regression model. Thus, the study showed that there are various predictors that can assess the QOL in cancer patients which can further serve as a guide to implement timely interventions to improve patients QOL.