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INTRODUCTION AND OBJECTIVES: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. PATIENTS AND METHODS: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n = 528 MASLD patients. RESULTS: In-sample model performance achieved AUROC curve 0.74-0.90 (95 % CI: 0.72-0.94), sensitivity 64 %-82 %, specificity 75 %-92 % and Positive Predictive Value (PPV) 94 %-98 %. Out-of-sample model validation had AUROC 0.70-0.86 (95 % CI: 0.67-0.90), sensitivity 69 %-70 %, specificity 96 %-97 % and PPV 75 %-77 %. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. CONCLUSIONS: A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.
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Registros Eletrônicos de Saúde , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Medição de Risco , Idoso , Prognóstico , Causas de Morte , Aprendizado Profundo , Fatores de Risco , Valor Preditivo dos Testes , Hepatopatia Gordurosa não Alcoólica/mortalidade , Hepatopatia Gordurosa não Alcoólica/diagnóstico , Adulto , Redes Neurais de Computação , Estudos RetrospectivosRESUMO
UNASSIGNED: Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the "RIC" version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies. (Rev Invest Clin. 2024;76(2):116-31).
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Carcinoma de Células Escamosas , Neoplasias Pulmonares , Neutrófilos , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/imunologia , Neoplasias Pulmonares/tratamento farmacológico , Prognóstico , Carcinoma de Células Escamosas/genética , Carcinoma de Células Escamosas/patologia , Carcinoma de Células Escamosas/imunologia , Carcinoma de Células Escamosas/tratamento farmacológico , Masculino , Feminino , Biomarcadores Tumorais/genética , Pessoa de Meia-Idade , Idoso , Regulação Neoplásica da Expressão Gênica , RNA Mensageiro/genéticaRESUMO
ABSTRACT Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the "RIC" version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies. (Rev Invest Clin. 2024;76(2):116-31)
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BACKGROUND: Breast cancer-related inflammation is critical in tumorigenesis, cancer progression, and patient prognosis. Several inflammatory markers derived from peripheral blood cells count, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII) are considered as prognostic markers in several types of malignancy. METHODS: We investigate and validate a prognostic model in early patients with breast cancer to predict disease-free survival (DFS) based on readily available baseline clinicopathological prognostic factors and preoperative peripheral blood-derived indexes. RESULTS: We analyzed a training cohort of 710 patients and 2 external validation cohorts of 980 and 157 patients with breast cancer, respectively, with different demographic origins. An elevated preoperative NLR is a better DFS predictor than others scores. The prognostic model generated in this study was able to classify patients into 3 groups with different risks of relapse based on ECOG-PS, presence of comorbidities, T and N stage, PgR status, and NLR. CONCLUSION: Prognostic models derived from the combination of clinicopathological features and peripheral blood indices, such as NLR, represent attractive markers mainly because they are easily detectable and applicable in daily clinical practice. More comprehensive prospective studies are needed to unveil their actual effectiveness.
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Neoplasias da Mama , Humanos , Feminino , Prognóstico , Neoplasias da Mama/patologia , Neutrófilos/patologia , Recidiva Local de Neoplasia/patologia , Linfócitos/patologia , Biomarcadores , Inflamação/patologia , Estudos RetrospectivosRESUMO
Abstract Introduction The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. Objective To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. Methods In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. Results A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p= 0.007), 2.1 (p= 0.009) and 2.47 (p= 0.01) for the 2-year OS, LFS and TRM, respectively. Conclusion This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.
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Humanos , Adulto , Leucemia , PrognósticoRESUMO
BACKGROUND: There is growing evidence that methylation-associated genes (MAGs) play an important role in the prognosis of acute myeloid leukemia (AML) patients. Thus, the aim of this research was to investigate the impact of MAGs in predicting the outcomes of AML patients. METHODS: The expression profile and clinical information of patients were downloaded from public databases. A novel prognostic model based on 7 MAGs was established in the TCGA training cohort and validated in the GSE71014 dataset. To validate the clinical implications, the correlation between MAGs signature and drug sensitivity was further investigated. RESULTS: 76 genes were screened out by the univariate Cox regression and significantly enriched in multiple methylation-related pathways. After filtering variables using LASSO regression analysis, 7 MAGs were introduced to construct the predictive model. The survival analysis showed overall survival of patients with the high-risk score was considerably poorer than that with the low-risk score in both the training and validating cohorts (p < 0.01). Furthermore, the risk score system as a prognostic factor also worked in the intermediate-risk patients based on ELN-2017 classification. Importantly, the risk score was demonstrated to be an independent prognostic factor for AML in the univariate and multivariate Cox regression analysis. Interestingly, GSEA analysis revealed that multiple metabolism-related pathways were significantly enriched in the high-risk group. Drug sensitivity analysis showed there was a significant difference in sensitivity of some drugs between the two groups. CONCLUSION: We developed a robust and accurate prognostic model with 7 MAGs. Our findings might provide a reference for the clinical prognosis and management of AML.
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Leucemia Mieloide Aguda , Humanos , Metilação , Prognóstico , Bases de Dados Factuais , Leucemia Mieloide Aguda/genética , Análise MultivariadaRESUMO
INTRODUCTION: The Acute Leukemia-European Society for Blood and Marrow Transplantation (AL-EBMT) risk score was recently developed and validated by Shouval et al. OBJECTIVE: To assess the ability of this score in predicting the 2-year overall survival (OS-2), leukemia-free survival (LFS-2) and transplant-related mortality (TRM) in acute leukemia (AL) adult patients undergoing a first allogeneic hematopoietic stem cell transplant (HSCT) at a transplant center in Brazil. METHODS: In this prospective, cohort study, we used the formula published by Shouval et al. to calculate the AL-EBMT score and stratify patients into three risk categories. RESULTS: A total of 79 patients transplanted between 2008 and 2018 were analyzed. The median age was 38 years. Acute myeloid leukemia was the most common diagnosis (68%). Almost a quarter of the cases were at an advanced stage. All hematopoietic stem cell transplantations (HSCTs) were human leukocyte antigen-matched (HLA-matched) and the majority used familial donors (77%). Myeloablative conditioning was used in 92% of the cases. Stratification according to the AL-EBMT score into low-, intermediate- and high-risk groups yielded the following results: 40%, 12% and 47% of the cases, respectively. The high scoring group was associated with a hazard ratio of 2.1 (p = 0.007), 2.1 (p = 0.009) and 2.47 (p = 0.01) for the 2-year OS, LFS and TRM, respectively. CONCLUSION: This study supports the ability of the AL-EBMT score to reasonably predict the 2-year post-transplant OS, LFS and TRM and to discriminate between risk categories in adult patients with AL, thus confirming its usefulness in clinical decision-making in this setting. Larger, multicenter studies may further help confirm these findings.
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The intracerebral hemorrhage (ICH) score and the ICH-grading scale (ICH-GS) are mortality predictor tools developed predominantly in conservatively treated ICH cohorts. We aimed to compare and evaluate the external validity of both models in predicting mortality in patients with ICH undergoing surgical intervention. A retrospective review of all patients presenting with spontaneous ICH admitted to a Peruvian national hospital between January 2018 and March 2020 was conducted. We compared the area under the receiver operating characteristic curve (AUC) for the ICH score and ICH-GS for in-hospital, 30-day, and 6-month mortality prediction. The research protocol was approved by the Institutional Review Board. A total of 73 patients (median age 62 years, 56.2% males) were included in the study. The mean ICH and ICH-GS scores were 2.5 and 8.7, respectively. In-hospital, 30-day, and 6-month mortality were 37%, 27.4%, and 37%, respectively. The AUC for in-hospital, 30-day, and 6-month mortality was 0.69, 0.71, and 0.69, respectively, for the ICH score and 0.64, 0.65, and 0.68, respectively, for the ICH-GS score. In this study, the ICH score and ICH-GS had moderate discrimination capacities to predict in-hospital, 30-day, and 6-month mortality in surgically treated patients. Additional studies should assess whether surgical intervention affects the discrimination of these prognostic models in order to develop predictive scores based on specific populations.
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Hemorragia Cerebral , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Peru/epidemiologia , Prognóstico , Curva ROC , Estudos RetrospectivosRESUMO
BACKGROUND: Neck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice. OBJECTIVE: The purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care. METHODS: This validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated. RESULTS: The Dmodel and Amodel discriminative performance (AUC < 0.70) and calibration measures (slope largely different from 1) were poor. The Mmodel could not be evaluated since several variables nor their proxies were available. CONCLUSIONS: External validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care.
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Cervicalgia , Traumatismos em Chicotada , Austrália , Humanos , PrognósticoRESUMO
BACKGROUND: There is currently no formal consensus on the administration of adjuvant chemotherapy to stage I lung squamous cell carcinoma (LUSC) patients despite the poor prognosis. The side effects of adjuvant chemotherapy need to be balanced against the risk of tumour recurrence. Prognostic markers are thus needed to identify those at higher risks and recommend individualised treatment regimens. METHODS: Clinical and sequencing data of stage I patients were retrieved from the Lung Squamous Cell Carcinoma project of the Cancer Genome Atlas (TCGA) and three tissue microarray datasets. In a novel K-resample gene selection algorithm, gene-wise Cox proportional hazard regressions were repeated for 50 iterations with random resamples from the TCGA training dataset. The top 200 genes with the best predictive power for survival were chosen to undergo an L1-penalised Cox regression for further gene selection. RESULTS: A total of 602 samples of LUSC were included, of which 42.2% came from female patients, 45.3% were stage IA cancer. From an initial pool of 11,212 genes in the TCGA training dataset, a final set of 12 genes were selected to construct the multivariate Cox prognostic model. Among the 12 selected genes, 5 genes, STAU1, ADGRF1, ATF7IP2, MALL and KRT23, were adverse prognostic factors for patients, while seven genes, NDUFB1, CNPY2, ZNF394, PIN4, FZD8, NBPF26 and EPYC, were positive prognostic factors. An equation for risk score was thus constructed from the final multivariate Cox model. The model performance was tested in the sequestered TCGA testing dataset and validated in external tissue microarray datasets (GSE4573, GSE31210 and GSE50081), demonstrating its efficacy in stratifying patients into high- and low-risk groups with significant survival difference both in the whole set (including stage IA and IB) and in the stage IA only subgroup of each set. The prognostic power remains significant after adjusting for standard clinical factors. When benchmarked against other prominent gene-signature based prognostic models, the model outperformed the rest in the TCGA testing dataset and in predicting long-term risk at eight years in all three validation datasets. CONCLUSION: The 12-gene prognostic model may serve as a useful complementary clinical risk-stratification tool for stage I and especially stage IA lung squamous cell carcinoma patients to guide clinical decision making.
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Carcinoma de Células Escamosas/genética , Perfilação da Expressão Gênica , Neoplasias Pulmonares/genética , Recidiva Local de Neoplasia/genética , Transcriptoma , Idoso , Idoso de 80 Anos ou mais , Benchmarking , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma de Células Escamosas/tratamento farmacológico , Carcinoma de Células Escamosas/mortalidade , Carcinoma de Células Escamosas/patologia , Quimioterapia Adjuvante/efeitos adversos , Feminino , Humanos , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/mortalidade , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Prognóstico , Modelos de Riscos Proporcionais , Estudos Retrospectivos , Carga TumoralRESUMO
RESUMEN Introducción: La predicción de fenómenos en las ciencias médicas mediante escalas pronósticas constituye una herramienta valiosa en la actualidad y deben incluirse en el proceso de toma de decisiones. Pronosticar la mediastinitis postoperatoria permite disponer de recursos para su prevención. Objetivo: Construir una escala pronóstica para estratificar el riesgo de padecer mediastinitis postoperatoria. Método: Se realizó un estudio de casos y controles para los factores de riesgo de mediastinitis postoperatoria en el Cardiocentro Ernesto Guevara de Santa Clara, Cuba. Luego de la regresión logística se obtuvo el modelo y, a partir de este, se incluyeron y ponderaron los predictores para obtener la escala cubana pronóstica de mediastinitis postoperatoria: PREDICMED, que se validó por diversos métodos. Resultados: Esta escala se obtuvo con seis predictores y dos estratos de riesgo. Se analizó su rendimiento mediante ajuste, calibración y determinación de su poder discriminante, con buenos resultados. Se realizó validación interna por el método de división de datos y se comparó su capacidad en ambos subconjuntos (desarrollo y validación) sin diferencias. Se probó su buena validez de constructo, al no existir diferencias entre las probabilidades predichas y las observadas. También se analizó su validez de contenido mediante expertos. Por último, se determinó su validez de criterio al comparar con otra escala similar (MEDSCORE). PREDICMED presentó muy buena capacidad discriminatoria (área bajo la curva 0,962) y elevados valores de sensibilidad (84,62%) y especificidad (92,31%). Conclusiones: La escala pronóstica cubana PREDICMED, para estratificar el riesgo de mediastinitis postoperatoria, mostró buenos parámetros de validación y logró estratificar el riesgo en no alto y alto.
ABSTRACT Introduction: Phenomena prediction through prognostic scales is a valuable tool in medical sciences nowadays and it should be included in the decision-making process. Predicting postoperative mediastinitis allows to count on resources for its prevention. Objective: To build a prognostic scale to stratify the risk of suffering from postoperative mediastinitis. Method: A case-control study for the risk factors of postoperative mediastinitis was carried out at the Cardiocentro Ernesto Guevara from Santa Clara, Cuba. After the logistic regression, the model was obtained and from it, the predictors to obtain the Cuban prognostic scale of postoperative mediastinitis PREDICMED were included and weighted, which was validated through several methods. Results: This scale was obtained, counting on six predictors and two risk strata. Its performance was analyzed through adjustment, calibration and determination of its discriminating capacity, showing good results. Internal validation was carried out through the data division method and its capacity was compared in both subsets (development and validation) showing no differences. Its good construct validity was demonstrated, since there were no differences between the predicted and the observed probabilities. Its contents validity was also analyzed by experts. Finally, its criteria validity was determined when compared with another similar scale (Medscore). PREDICMED showed a very good discriminatory capacity (area under the curve 0.962) as well as high values of sensitivity (84.62%) and specificity (92.31%). Conclusions: The Cuban prognostic scale PREDICMED, to stratify the risk of postoperative mediastinitis showed good validation parameters and it was able to stratify the risk in not high and high.
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Cirurgia Torácica , Estudo de Validação , Previsões , MediastiniteRESUMO
INTRODUCTION: By definition, hypertensive cardiopathy is a series of complex and variable effects responsible for the chronic elevation of blood pressure in the heart. It stands out within a broad spectrum of cardiovascular diseases associated with hypertension. OBJECTIVE: To evaluate the capacity to predict the development of adaptive changes to hypertensive cardiopathy within ten years following diagnosis of the condition, using a model based on prognostic factors. METHODS: A prospective cohort study was conducted in hypertensive patients. The patients were followed at the specialized hypertension physicians office of the specialty policlinic attached to Carlos Manuel de Céspedes University Hospital, in the Bayamo Municipality, Granma Province, Cuba, from 1 January 2008 to 31 December 2018. RESULTS: Coxs proportional regression model showed a significant statistical relationship between most of the factors and the development of the adaptive changes in hypertensive cardiopathy within ten years of follow-up after the diagnosis of this condition. The lack of blood pressure control (Hazard ratio: 2.090; confidence interval 95%: 1.688 to 2.588; p: 0.000) followed by stage 2 of hypertension (hazard ratio: 1.987; confidence interval 95%: 1.584 to 2.491; p: 0.000) were the main factors. Internal validation of the model, discriminant capacity (C- statistic: 0.897) and calibration Hosmer-Lemeshow (χ2: 5.384; p: 0.716), was acceptable. CONCLUSIONS: We develop a model to predict the progression of hypertensive cardiopathy from grade I to grade IV with adequate discriminatory capacity. The model is based on prognostic factors, among which characteristic effects of arterial hypertension, diabetes mellitus, and chronic kidney disease stood out.
INTRODUCCIÓN: La cardiopatía hipertensiva sobresale dentro del amplio espectro de las enfermedades cardiovasculares asociadas con la hipertensión. Esta es definida como un complejo y variable conjunto de efectos que provoca en el corazón la elevación crónica de la presión arterial. OBJETIVO: Evaluar la capacidad de vaticinar el desarrollo de los cambios evolutivos de la cardiopatía hipertensiva en los diez años siguientes al diagnóstico, mediante un modelo basado en factores pronósticos. MÉTODOS: Realizamos un estudio de cohortes, prospectivo en pacientes hipertensos atendidos en la consulta especializada de hipertensión arterial de la policlínica de especialidades del Hospital General Universitario Carlos Manuel de Céspedes del municipio de Bayamo, Cuba, desde el uno de enero de 2008 hasta el 31 de diciembre de 2018. RESULTADOS: El modelo de riesgos proporcionales de Cox mostró una relación estadística significativa entre la mayoría de los factores y el desarrollo de los cambios evolutivos de la cardiopatía hipertensiva en los diez años siguientes al diagnóstico. El lugar más relevante lo ocupó la falta de control de la presión arterial (Hazard ratio: 2,090; intervalo de confianza 95%: 1,688 a 2,588; p = 0,000) seguido al de clasificar en el estadio dos de la hipertensión arterial (Hazard ratio: 1,987; intervalo de confianza 95%: 1,584 a 2,491; p = 0,000). La validez interna del modelo fue adecuada: la capacidad discriminativa (estadístico C = 0,897) y la calibración (χ2: 5,384; p = 0,716). CONCLUSIONES: Se obtiene un modelo para pronosticar la progresión de la cardiopatía hipertensiva de grado I a grado IV, con capacidad discriminativa y calibración adecuadas a partir de factores pronósticos. Entre estos últimos sobresalen los efectos propios de la hipertensión arterial, la diabetes mellitus y enfermedad renal crónica.
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Cardiopatias/fisiopatologia , Hipertensão/fisiopatologia , Pressão Sanguínea , Estudos de Coortes , Cuba , Diástole/fisiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Estudos Prospectivos , Análise de Regressão , Índice de Gravidade de Doença , Sístole/fisiologiaRESUMO
An early discrimination of survival probability is required for patients with diffuse large B cell lymphoma (DLBCL), which may identify patients that require other treatment options, for example clinical trials. To the best of our knowledge, the impact of interim evaluation with 18fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) has not yet been determined in this type of neoplasia. The aim of the present study was to determine the role of changes in metabolic tumor volume (MTV) between baseline and interim 18F-FDG PET/CT scans, following three courses of chemotherapy in order to predict complete response (CR) and overall survival (OS) in patients with DLBCL. Patients with previously untreated DLBCL who had received the standard 6-8 cycles of rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone were included in the present study. A predictive model was constructed using changes in MTV and other clinical factors including age, gender, East Cooperative Oncology Group (ECOG) status, clinical stage, B symptoms, the presence of bulky disease and elevated lactate dehydrogenase levels, and data were analyzed using logistic regression analysis. In total, 50 patients with DLBCL were included in the present study. The majority of patients presented with stage III/IV disease (64%), B symptoms (72%) and bulky disease (58%). According to the International Prognostic Index score, 44% of patients were in the intermediate-high or high-risk categories for risk of relapse, and therefore considered to have poor prognosis. In total, ≥94% of patients achieving a decrease in total MTV had a 2-year OS rate of 95%, compared with the 58% OS rate of those with a suboptimal response. A multivariate model, including a change in MTV (a decrease of ≥94%), the ECOG performance status ≥2, a change in leukocyte counts and age, was used to predict CR. This model was used to define two groups according to the predicted probability of recurrence (cutoff, 0.69). The 2-year survival rates of the two groups were 95 and 59%, respectively. Analysis of changes in MTV in the interim 18F-FDG PET/CT revealed significant prognostic value for the prediction of CR and OS in patients with DLBCL.
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BACKGROUND: The South African Triage Scale (SATS) was developed to facilitate patient triage in emergency departments (EDs) and is used by Médecins Sans Frontières (MSF) in low-resource environments. The aim was to determine if SATS data, reason for admission, and patient age can be used to develop and validate a model predicting the in-hospital risk of death in emergency surgical centers and to compare the model's discriminative power with that of the four SATS categories alone. METHODS: We used data from a cohort hospitalized at the Nap Kenbe Surgical Hospital in Haiti from January 2013 to June 2015. We based our analysis on a multivariate logistic regression of the probability of death. Age cutoff, reason for admission categorized into nine groups according to MSF classifications, and SATS triage category (red, orange, yellow, and green) were used as candidate parameters for the analysis of factors associated with mortality. Stepwise backward elimination was performed for the selection of risk factors with retention of predictors with P < 0.05, and bootstrapping was used for internal validation. The likelihood ratio test was used to compare the combined and restricted models. These models were also applied to data from a cohort of patients from the Kunduz Trauma Center, Afghanistan, to validate mortality prediction in an external trauma patients population. RESULTS: A total of 7618 consecutive hospitalized patients from the Nap Kenbe Hospital were analyzed. Variables independently associated with in-hospital mortality were age > 45 and < = 65 years (odds ratio, 2.04), age > 65 years (odds ratio, 5.15) and the red (odds ratio, 65.08), orange (odds ratio, 3.5), and non-trauma (odds ratio, 3.15) categories. The combined model had an area under the receiver operating characteristic curve (AUROC) of 0.8723 and an AUROC corrected for optimism of 0.8601. The AUROC of the model run on the external data-set was 0.8340. The likelihood ratio test was highly significant in favor of the combined model for both the original and external data-sets. CONCLUSIONS: SATS category, patient age, and reason for admission can be used to predict in-hospital mortality. This predictive model had good discriminative ability to identify ED patients at a high risk of death and performed better than the SATS alone.
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Mortalidade Hospitalar/tendências , Centros de Traumatologia , Triagem , Adolescente , Adulto , Afeganistão , Idoso , Idoso de 80 Anos ou mais , Criança , Pré-Escolar , Técnicas de Apoio para a Decisão , Feminino , Haiti , Humanos , Lactente , Recém-Nascido , Modelos Logísticos , Masculino , Pessoa de Meia-Idade , Razão de Chances , Curva ROC , Estudos Retrospectivos , Fatores de Risco , Triagem/normas , Adulto JovemRESUMO
Introducción: En presencia de complicaciones infecciosas intrabdominales postoperatorias, la decisión de reoperar es todavía difícil para el cirujano actuante. Los modelos matemáticos representan una buena ayuda al diagnóstico en estas condiciones. Método: Estudio prospectivo observacional de 300 pacientes post-cirugía abdominal ingresados en la unidad de cuidados intensivos del Hospital Calixto García desde enero de 2008 a enero de 2010. Los pacientes fueron aleatoriamente separados (2:1) en dos grupos; estimación (GE) y validación (GV). En el GE se desarrollaron tres modelos estadísticos para la reoperación, que fueron validados en el GV .Estos modelos incluyeron variables, que en estudios anteriores demostraron su utilidad en el pronóstico, como el índice predictivo de reoperación aguda (ARPI) y la presión intrabdominal (PIA) Resultados: El modelo ARPI-PIA fue el mejor de los tres modelos, según el estadígrafo Hosmer-Lemeshow (calibración C=9,976 p=0.267, discriminación área bajo la curva ROC=0,989 IC 95 por ciento 0,976-1,000). Conclusión: La inclusión de la PIA junto al ARPI en un modelo matemático puede aumentar la certeza del pronóstico de reoperación en presencia de complicaciones infecciosas intrabdominales tras cirugía abdominal. Este modelo puede ser de utilidad en situaciones de recursos diagnósticos limitados.(AU)
Background: The decision of re-operating after abdominal surgery is still difficult, especially whenan intra-abdominal infectious complication is present. Mathematical models represent good diagnosis aid. Methods: A prospective observational study was conducted with 300 patients after abdominal surgery consecutively admitted at the intensive care unit of the "CalixtoGarcía Hospital" from January 2008 to January 2010. The patients were randomly separated (2:1) into estimation and validation groups. Three models for re-operation were developed in the estimation group by logistic regression, using some factors that demonstrated their usefulness in previous studies, for example, the acute re-operation predictive index (ARPI) and the intra-abdominal pressure (IAP). Afterwords, the models were validated on the other group. Results: Acute re-operation predictive index-intraabdominal pressure (ARPI-IAP) model was the best of the three models, with an excellent calibration by the Hossmer-Lemeshow goodness-of fit statistic (C=9,976 p=0,267), discrimination (AUC=0,989 95 percent CI 0,976-1,000). Conclusion: The combination of IAP with ARPI in a mathematical model can add accuracy to the prediction of re-operation related to intra-abdominal infectious complications in patients after abdominal surgery. This model is recommended in conditions of limited diagnostic resources. (AU)