scholarly journals Predicting 6-Month Mortality in Incident Elderly Dialysis Patients: A Simple Prognostic Score

2019 ◽  
Vol 45 (1) ◽  
pp. 38-50
Author(s):  
Josefina Santos ◽  
Pedro Oliveira ◽  
Jorge Malheiro ◽  
Andreia Campos ◽  
Sofia Correia ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Prognostic Score ◽  
Treatment Options ◽  
Laboratory Data ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Score ◽  
Multivariable Logistic Regression Analysis ◽  
Patient Centered ◽  
Internal Validation

Aim: Mortality in end-stage renal disease (ESRD) remains high, particularly among elderly, who represents the most rapidly growing segment of the ESRD population in wealthier countries. We developed and validated a risk score in elderly patients to predict 6-month mortality after dialysis initiation. Methods: We used data from a cohort of 421 patients, aged 65 years and over who started dialysis between 2009 and 2016, in our Nephrology department. The predictive score was developed using a multivariable logistic regression analysis. A bootstrapping technique was used for internal validation. Results: The overall mortality within 6 months was 14.0%. Five independent predictors were identified, and a points system was constructed: age 75 years or older (2 points), coronary artery disease (2), cerebrovascular disease with hemiplegia (2), time of nephrology care before dialysis (<3.0 months [2]; ≥3 to <12 months [1]), and serum albumin levels (3.0–3.49 g/dL [1]; <3.0 g/dL [2]). A score of 6 identified patients with a 70% risk of 6-month mortality. Model performance was good in both discrimination (area under the curve of 0.793; [95% CI 0.73–0.86]) and validation (concordance statistics of 0.791 [95% CI 0.73–0.85]). Conclusions: We developed a simple prediction score based on readily available clinical and laboratory data that can be a practical and useful tool to assess short-term prognosis in elderly patients starting dialysis. It may help to inform patients and their families about ESRD treatment options and provide a more patient-centered overall approach to care.

scholarly journals Use of serum biomarkers in staging of canine hepatic fibrosis

2019 ◽  
Vol 31 (5) ◽  
pp. 665-673 ◽  
Author(s):  
Maud Menard ◽  
Alexis Lecoindre ◽  
Jean-Luc Cadoré ◽  
Michèle Chevallier ◽  
Aurélie Pagnon ◽  
...  
Keyword(s):  
Liver Biopsy ◽  
Hepatic Fibrosis ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Gamma Glutamyl Transferase ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set ◽  
Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

scholarly journals The Emergency Surgery Frailty Index (EmSFI): development and internal validation of a novel simple bedside risk score for elderly patients undergoing emergency surgery

2020 ◽  
Author(s):  
Gianluca Costa ◽  
◽  
Laura Bersigotti ◽  
Giulia Massa ◽  
Luca Lepre ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Surgical Procedures ◽  
Emergency Surgery ◽  
Characteristic Curve ◽  
Frailty Index ◽  
Univariate Analysis ◽  
The Elderly ◽  
Predictive Score ◽  
Internal Validation ◽  
Discrimination Ability

Abstract Background Frailty assessment has acquired an increasing importance in recent years and it has been demonstrated that this vulnerable profile predisposes elderly patients to a worse outcome after surgery. Therefore, it becomes paramount to perform an accurate stratification of surgical risk in elderly undergoing emergency surgery. Study design 1024 patients older than 65 years who required urgent surgical procedures were prospectively recruited from 38 Italian centers participating to the multicentric FRAILESEL (Frailty and Emergency Surgery in the Elderly) study, between December 2016 and May 2017. A univariate analysis was carried out, with the purpose of developing a frailty index in emergency surgery called “EmSFI”. Receiver operating characteristic curve analysis was then performed to test the accuracy of our predictive score. Results 784 elderly patients were consecutively enrolled, constituting the development set and results were validated considering further 240 consecutive patients undergoing colorectal surgical procedures. A logistic regression analysis was performed identifying different EmSFI risk classes. The model exhibited good accuracy as regard to mortality for both the development set (AUC = 0.731 [95% CI 0.654–0.772]; HL test χ2 = 6.780; p = 0.238) and the validation set (AUC = 0.762 [95% CI 0.682–0.842]; HL test χ2 = 7.238; p = 0.299). As concern morbidity, our model showed a moderate accuracy in the development group, whereas a poor discrimination ability was observed in the validation cohort. Conclusions The validated EmSFI represents a reliable and time-sparing tool, despite its discriminative value decreased regarding complications. Thus, further studies are needed to investigate specifically surgical settings, validating the EmSFI prognostic role in assessing the procedure-related morbidity risk.

Elaboration of a new synovial predictive score of septic origin for acute arthritis on the native joint (RESAS)

Rheumatology ◽  
2020 ◽  
Author(s):  
Guillaume Coiffier ◽  
Olivia Berthoud ◽  
Jean David Albert ◽  
François Robin ◽  
Claire Goussault ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
External Validation ◽  
White Blood Cells ◽  
Area Under The Curve ◽  
Predictive Score ◽  
Internal Validation ◽  
Acute Arthritis ◽  
Independent Variables ◽  
A Value ◽  
Independent Cohort

Abstract Objective To establish a new predictive score for the diagnosis of septic arthritis (SA) according to different synovial fluid (SF) variables. Methods First, we analysed the different clinical, biological and SF variables associated with the diagnosis of SA (according to the Newman’s criteria) in a monocentric cohort of acute arthritis (&lt;30 days) (n = 233) (SYNOLACTATE cohort). A new score predictive of SA (RESAS) was created using the independent discriminant variables after multivariate analysis. A value was attributed to each variable of the score according to the weighting based on their likelihood ratio for the diagnosis of SA. RESAS performance was then tested on the first cohort (internal validation) and then checked on a second independent cohort (n = 70) (external validation). Results After multivariate analysis, four independent variables of the SF were included for RESAS: (i) purulent SF or white blood cells count ≥70 000/mm3; (ii) absence/presence of crystals; (iii) lactate; and (iv) glucose synovial level. RESAS ranged between −4 and +13 points. The performance of RESAS to predicted SA was excellent with area under the curve (AUC)=0.928 (0.877–0.980) in internal validation and AUC=0.986 (0.962–1.00) in external validation. For a RESAS threshold ≥+4, SA was diagnosed with Se=56.0% (0.371–0.733), Sp=98.1% (0.952–0.993), LR+=29.1 (10.4–81.6) in the first cohort and with Se=91.7% (0.646–0.985), Sp=98.3% (0.909–0.997), LR+=53.2 (7.56–373) in the second cohort. Conclusion RESAS is a new composite score of four SF variables with excellent performance to predicted SA in acute arthritis population.

A MRI-based radiomics model for preoperative prediction of five-year survival status in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15596-e15596
Author(s):  
Xiao-Hang Wang ◽  
Liu-Hua Long ◽  
Yong Cui ◽  
Angela Y Jia ◽  
Xiang-Gao Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Model Performance ◽  
Area Under The Curve ◽  
Clinical Risk Factor ◽  
Multivariable Logistic Regression Analysis ◽  
Survival Status ◽  
Clinical Risk ◽  
Preoperative Prediction ◽  
Magnetic Resonance Imaging Mri ◽  
Radiomics Signature

e15596 Background: Recurrence is the major cause of mortality in resected hepatocellular carcinoma (HCC) patients. However, without a standard approach to evaluate prognosis, it is difficult to select potential candidates for additional therapy. We aim to develop and evaluate a magnetic resonance imaging (MRI)-based radiomics model to predict 5-year survival status of HCC patients in the preoperative setting. Methods: A total of 201 HCC patients who were followed up for at least 5 years (unless death occurred) after curative hepatectomy were enrolled in this retrospective multicenter study. 3144 radiomics features were extracted from four conventional sequences of preoperative MRI (T1WI, T2WI, DWI and dynamic contrast-enhanced MRI). The random forest method was used for feature selection and radiomics signature building. 5-fold cross validation was used for robust estimation. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. The model performance was evaluated by its discrimination and calibration. Results: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on survival status at 5 years from surgery. The 30 most survival-related radiomics features were selected to develop the radiomics signature. The preoperative alpha-fetoprotein level was integrated into the model as an independent clinical risk factor in multivariable logistic regression analysis (OR = 3.764; 95% CI 1.997-7.096). The radiomics model demonstrated good calibration and satisfactory discrimination, with the mean area under the curve of 0.9340 (95% CI 0.9222-0.9458) in training set and 0.7383 (95% CI 0.6914-0.7852) in validation set. Conclusions: The MRI-based radiomics model represents a valid method to predict 5-year survival status in HCC patients in the preoperative setting, and may be used to guide neoadjuvant or adjuvant treatment decisions in high-risk patients.

scholarly journals A Prediction Model of Sufficient Filter Lifespan in Anticoagulation-free CRRT Patients

2020 ◽  
Author(s):  
Wei Zhang ◽  
Ming Bai ◽  
Ling Zhang ◽  
Yan Yu ◽  
Yangping Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Predictive Value ◽  
Primary Outcome ◽  
Area Under The Curve ◽  
Bleeding Risk ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Prediction ◽  
Internal Validation ◽  
Clinical Prediction Model ◽  
Final Model

Abstract Background: Anticoagulation-free continuous renal replacement therapy (CRRT) was recommended by the current clinical guideline for patients with increased bleeding risk and contraindications of citrate and resulted in heterogeneous filter lifespan. There was no prediction model to identify the patients would have sufficient filter lifespan when they have to accept CRRT without the use of any anticoagulation. The purpose of our present study is to develop a clinical prediction model of sufficient filter lifespan in anticoagulation-free CRRT patients.Method: Patients who underwent anticoagulation-free CRRT in our center between June 2013 and June 2019 were retrospectively included. The primary outcome was sufficient filter lifespan (≥ 24 hours). The final model was established by using multivariable logistic regression analysis. And, the prediction model was validated in an external cohort. Results: A total of 170 patients were included in the development cohort. Sufficient filter lifespan were observed in 80 patients. The probability of sufficient filter lifespan could be calculated using the following regression formula: P (%) = exp (Z)/1 + exp (Z), where Z = 0.49896-(0.08552*BMI)+(0.44107*T)+(0.03373*MAP)-(0.03389*WBC)+(1.51579*[vasopressor=1])-(0.01132*PLT)+(0.00422*ALP)-(2.66910*pH)-(0.00214*UA)+(0.05992*BUN)+(0.00400*Db)–(0.00014*D-dimer)+(0.02818*APTT). The area under the curve (AUC) of the stepwise model and internal validation model was 0.82 (95%CI [0.76-0.88]) and 0.8 (95%CI [0.74-0.87]), respectively. At the optimal cut-off value of -0.1052, the positive predictive value and the negative predictive value of the stepwise model was 0.77 and 0.79, respectively. The AUC of the external model was 0.82 (95%CI [0.69-0.96]). Conclusion: The use of a prediction model instead of an assessment based only on coagulation parameters could facilitate the identification of the patients with filter lifespan of ≥ 24 hours when they accepted anticoagulation-free CRRT.

scholarly journals Development and internal validation of a simple prognostic score for early sepsis risk stratification in the emergency department

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e046009
Author(s):  
Bofu Liu ◽  
Dongze Li ◽  
Yisong Cheng ◽  
Jing Yu ◽  
Yu Jia ◽  
...  
Keyword(s):  
Emergency Department ◽  
Characteristic Curve ◽  
Prognostic Score ◽  
Cohort Analysis ◽  
Area Under The Curve ◽  
Respiratory Arrest ◽  
Apache Ii ◽  
Net Benefit ◽  
Internal Validation ◽  
Physiological Indicators

ObjectivesNo validated, simple, powerful and continuously monitorable risk prediction tools are available for patients with sepsis during the early phases in the emergency department (ED). We sought to derive a novel Simple Sepsis Early Prognostic Score (SSEPS) composed of physiological indicators that do not depend on laboratory tests and that can be used by emergency clinicians in predicting outcomes in patients with sepsis.DesignRetrospective cohort analysis of a collected data source.ParticipantsPatients with sepsis admitted to the ED of the West China Hospital of Sichuan University between July 2015 and June 2016 were included. We excluded patients who were pregnant, those with cardiac or respiratory arrest, and those using vasoactive drugs before admission to the ED.Primary outcome measures28-day all-cause mortality.ResultsThe SSEPS consisted of age, heart rate, respiratory rate and altered consciousness. Patients in the development cohort with higher SSEPS had a significantly higher mortality (first tertile vs second tertile vs third tertile: 12.5% vs 28.6% vs 53.5%, p<0.001). The area under the receiver operating characteristic curve for SSEPS was 0.762 (95% CI 0.686 to 0.838), which was similar to Sequential Organ Failure Assessment (SOFA) (area under the curve: 0.745, 95% CI 0.692 to 0.798) and Acute Physiology and Chronic Health Evaluation (APACHE II) (area under the curve: 0.750, 95% CI 0.681 to 0.819). Moreover, the decision curve analysis showed that the net benefit of SSEPS was higher than SOFA and APACHE II at any probability threshold.ConclusionThe SSEPS is simple and useful for clinicians in stratifying high-risk patients with sepsis at the early phase of ED admission.

scholarly journals Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Jianqiu Kong ◽  
Junjiong Zheng ◽  
Jieying Wu ◽  
Shaoxu Wu ◽  
Jinhua Cai ◽  
...  
Keyword(s):  
External Validation ◽  
Area Under The Curve ◽  
Multivariable Logistic Regression Analysis ◽  
Lasso Regression ◽  
Clinical Predictors ◽  
Internal Validation ◽  
Clinical Model ◽  
Magnetic Resonance Imaging Mri ◽  
Individualized Prediction ◽  
Radiomics Signature

Abstract Background Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. Methods In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. Results Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. Conclusion We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

Predictors of Purchasing a Hearing Aid After an Evaluation Period: A Prospective Study in Dutch Older Hearing Aid Candidates

2019 ◽  
Vol 28 (3S) ◽  
pp. 802-805 ◽  
Author(s):  
Marieke Pronk ◽  
Janine F. J. Meijerink ◽  
Sophia E. Kramer ◽  
Martijn W. Heymans ◽  
Jana Besser
Keyword(s):  
Help Seeking ◽  
Hearing Aid ◽  
Model Performance ◽  
Area Under The Curve ◽  
Secondary Data ◽  
Support Program ◽  
Evaluation Period ◽  
A Prospective Study ◽  
Study Participants ◽  
Baseline Covariates

Purpose The current study aimed to identify factors that distinguish between older (50+ years) hearing aid (HA) candidates who do and do not purchase HAs after having gone through an HA evaluation period (HAEP). Method Secondary data analysis of the SUpport PRogram trial was performed ( n = 267 older, 1st-time HA candidates). All SUpport PRogram participants started an HAEP shortly after study enrollment. Decision to purchase an HA by the end of the HAEP was the outcome of interest of the current study. Participants' baseline covariates (22 in total) were included as candidate predictors. Multivariable logistic regression modeling (backward selection and reclassification tables) was used. Results Of all candidate predictors, only pure-tone average (average of 1, 2, and 4 kHz) hearing loss emerged as a significant predictor (odds ratio = 1.03, 95% confidence interval [1.03, 1.17]). Model performance was weak (Nagelkerke R 2 = .04, area under the curve = 0.61). Conclusions These data suggest that, once HA candidates have decided to enter an HAEP, factors measured early in the help-seeking journey do not predict well who will and will not purchase an HA. Instead, factors that act during the HAEP may hold this predictive value. This should be examined.

Preoperative Circulating MiR-210, a Risk Factor for Postoperative Delirium Among Elderly Patients with Gastric Cancer Undergoing Curative Resection

2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Elderly Patients ◽  
Curative Resection ◽  
Postoperative Delirium ◽  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Clinicopathologic Characteristics

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.

Alanine Aminotransferase Predicts Outcomes in Elderly Patients with Aneurysmal Subarachnoid Hemorrhage

2019 ◽  
Vol 16 (1) ◽  
pp. 89-95
Author(s):  
Jianfeng Zheng ◽  
Rui Xu ◽  
Zongduo Guo ◽  
Xiaochuan Sun
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Elderly Patients ◽  
Clinical Outcomes ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Laboratory Data ◽  
Cardiac Complications ◽  
World Population ◽  
Systemic Complications ◽  
Poor Outcome ◽  
Poor Outcomes

Objective: With the aging of the world population, the number of elderly patients suffering from aneurysmal subarachnoid hemorrhage (aSAH) is gradually growing. We aim to investigate the potential association between plasma ALT level and clinical complications of elderly aSAH patients, and explore its predictive value for clinical outcomes of elderly aSAH patients. Methods: Between January 2013 and March 2018, 152 elderly aSAH patients were analyzed in this study. Clinical information, imaging findings and laboratory data were reviewed. According to the Glasgow Outcome Scale (GOS), clinical outcomes at 3 months were classified into favorable outcomes (GOS 4-5) and poor outcomes (GOS 1-3). Logistic regression analysis was used to assess the indicators associated with poor outcomes, and receiver curves (ROC) and corresponding area under the curve (AUC) were used to detect the accuracy of the indicator. Results: A total of 48 (31.6 %) elderly patients with aSAH had poor outcome at 3 months. In addition to ICH, IVH, Hunt-Hess 4 or 5 Grade and Modified Fisher 3 or 4 Grade, plasma ALT level was also strongly associated with poor outcome of elderly aSAH patients. After adjusting for other covariates, plasma ALT level remained independently associated with pulmonary infection (OR 1.05; 95% CI 1.00–1.09; P = 0.018), cardiac complications (OR 1.05; 95% CI 1.01–1.08; P = 0.014) and urinary infection (OR 1.04; 95% CI 1.00–1.08; P = 0.032). Besides, plasma ALT level had a predictive ability in the occurrence of systemic complications (AUC 0.676; 95% CI: 0.586– 0.766; P<0.001) and poor outcome (AUC 0.689; 95% CI: 0.605–0.773; P<0.001) in elderly aSAH patients. Conclusion: Plasma ALT level of elderly patients with aSAH was significantly associated with systemic complications, and had additional clinical value in predicting outcomes. Given that plasma ALT levels on admission could help to identify high-risk elderly patients with aSAH, these findings are of clinical relevance.

Sign in / Sign up

Export Citation Format

Share Document