Evaluating hospital performance based on excess cause-specific incidence

Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry

Prognostic factors of survival time after hematopoietic stem cell transplant in acute lymphoblastic leukemia patients: Cox proportional hazard versus accelerated failure time models

<p>Abstract</p> <p>Background</p> <p>The aim of this study is to evaluate the prognostic factors of overall survival (OS) after haematopoietic stem cell transplant (HSCT) in acute lymphoblastic leukaemia (ALL) patients using accelerated failure time (AFT), Cox proportional hazard (PH), and Cox time-varying coefficient models.</p> <p>Methods</p> <p>206 patients were enrolled after HSCH in Shariati Hospital between 1993 and 2007. There was evidence of marked departures from the proportional hazards assumption with two prognostic factors, relapse and chronic graft-versus-host disease (cGVHD) (P < .001). Performance among AFT and Cox's models was assessed using explained variation and goodness of fit methods. Discrimination among the exponential, Weibull, generalized gamma (GG), log-logistic, and lognormal distributions was done using maximum likelihood and Akaike information criteria.</p> <p>Results</p> <p>The 5-year OS was 52% (95%CI: 47.3–56.7). Peak mortality hazard occurred at months 6–7 after HSCT followed by a decreasing trend. In univariate analysis, the data was better fitted by GG distribution than by other distributions. Univariate analysis using GG distribution showed a positive association between OS with acute graft-versus-host disease (aGVHD) (P = .021), no relapse (P < .001), cGVHD (P < .001), neutrophil recovery (P < .001) and platelet recovery (P < .001). Based on Cox PH models; however cGVHD and relapse were the predictive factors of OS (P < .001). Multivariate analysis indicated that, OS is related to relapse (P < .001) and platelet recovery (P = .037), where predictive power of Weibull AFT models was superior to Cox PH model and Cox with time-varying coefficient (R²= 0.46 for AFT, R²= .21 for Cox PH and R²= .34 for Cox time-varying coefficient). Cox-Snell residual shows Weibull AFT fitted to data better than other distributions in multivariate analysis.</p> <p>Conclusion</p> <p>We concluded that AFT distributions can be a useful tool for recognizing prognostic factors of OS in acute lymphoblastic leukemia patients.</p

Predictive accuracy of novel risk factors and markers: A simulation study of the sensitivity of different performance measures for the Cox proportional hazards regression model

Predicting outcomes that occur over time is important in clinical, population health, and health services research. We compared changes in different measures of performance when a novel risk factor or marker was added to an existing Cox proportional hazards regression model. We performed Monte Carlo simulations for common measures of performance: concordance indices (c, including various extensions to survival outcomes), Royston's D index, R2-type measures, and Chambless' adaptation of the integrated discrimination improvement to survival outcomes. We found that the increase in performance due to the inclusion of a risk factor tended to decrease as the performance of the reference model increased. Moreover, the increase in performance increased as the hazard ratio or the prevalence of a binary risk factor increased. Finally, for the concordance indices and R2-type measures, the absolute increase in predictive accuracy due to the inclusion of a risk factor was greater when the observed event rate was higher (low censoring). Amongst the different concordance indices, Chambless and Diao's c-statistic exhibited the greatest increase in predictive accuracy when a novel risk factor was added to an existing model. Amongst the different R2-type measures, O'Quigley et al.'s modification of Nagelkerke's R2 index and Kent and O'Quigley's Ï w, a 2 displayed the greatest sensitivity to the addition of a novel risk factor or marker. These methods were then applied to a cohort of 8635 patients hospitalized with heart failure to examine the added benefit of a point-based scoring system for predicting mortality after initial adjustment with patient age alone

Biomarkers and low risk in heart failure. Data from COACH and TRIUMPH

Aim Traditionally, risk stratification in heart failure (HF) emphasizes assessment of high risk. We aimed to determine if biomarkers could identify patients with HF at low risk for death or HF rehospitalization. Methods and results This analysis was a substudy of The Coordinating Study Evaluating Outcomes of Advising and Counselling in Heart Failure (COACH) trial. Enrolment of HF patients occurred before discharge. We defined low risk as the absence of death and/or HF rehospitalizations at 180 days. We tested a diverse group of 29 biomarkers on top of a clinical risk model, with and without N-terminal pro-B-type natriuretic peptide (NT-proBNP), and defined the low risk biomarker cut-off at the 10th percentile associated with high positive predictive value. The best performing biomarkers together with NT-proBNP and cardiac troponin I (cTnI) were re-evaluated in a validation cohort of 285 HF patients. Of 592 eligible COACH patients, the mean (± SD) age was 71 (± 11) years and median (IQR) NT-proBNP was 2521 (1301-5634) pg/mL. Logistic regression analysis showed that only galectin-3, fully adjusted, was significantly associated with the absence of events at 180 days (OR 8.1, 95% confidence interval 1.06-50.0, P = 0.039). Galectin-3, showed incremental value when added to the clinical risk model without NT-proBNP (increase in area under the curve from 0.712 to 0.745, P = 0.04). However, no biomarker showed significant improvement by net reclassification improvement on top of the clinical risk model, with or without NT-proBNP. We confirmed our results regarding galectin-3, NT-proBNP, and cTnI in the independent validation cohort. Conclusion We describe the value of various biomarkers to define low risk, and demonstrate that galectin-3 identifies HF patients at (very) low risk for 30-day and 180-day mortality and HF rehospitalizations after an episode of acute HF. Such patients might be safely discharged

Prognostic importance of plasma total magnesium in a cohort of cats with azotemic chronic kidney disease

BACKGROUND: Hypomagnesemia is associated with increased mortality and renal function decline in humans with chronic kidney disease (CKD). Magnesium is furthermore inversely associated with fibroblast growth factor 23 (FGF23), an important prognostic factor in CKD in cats. However, the prognostic significance of plasma magnesium in cats with CKD is unknown. OBJECTIVES: To explore associations of plasma total magnesium concentration (tMg) with plasma FGF23 concentration, all-cause mortality, and disease progression in cats with azotemic CKD. ANIMALS: Records of 174 client-owned cats with IRIS stage 2-4 CKD. METHODS: Cohort study. Cats with azotemic CKD were identified from the records of two London-based first opinion practices (1999-2013). Possible associations of baseline plasma tMg with FGF23 concentration and risks of death and progression were explored using, respectively, linear, Cox, and logistic regression. RESULTS: Plasma tMg (reference interval, 1.73-2.57 mg/dL) was inversely associated with plasma FGF23 when controlling for plasma creatinine and phosphate concentrations (partial correlation coefficient, -0.50; P < .001). Hypomagnesemia was observed in 12% (20/174) of cats, and independently associated with increased risk of death (adjusted hazard ratio, 2.74; 95% confidence interval [CI], 1.35-5.55; P = .005). The unadjusted associations of hypermagnesemia (prevalence, 6%; 11/174 cats) with survival (hazard ratio, 2.88; 95% CI, 1.54-5.38; P = .001), and hypomagnesemia with progressive CKD (odds ratio, 17.7; 95% CI, 2.04-154; P = .009) lost significance in multivariable analysis. CONCLUSIONS AND CLINICAL IMPORTANCE: Hypomagnesemia was associated with higher plasma FGF23 concentrations and increased risk of death. Measurement of plasma tMg augments prognostic information in cats with CKD, but whether these observations are associations or causations warrants further investigation

Overall survival and biochemical failure-free survival comparison of brachytherapy treatment options versus external beam radiation therapy for both low and intermediate-risk prostate cancer: A propensity-score matched analysis

Purpose: This study compares overall survival (OS) and biochemical failure-free survival (bFFS) in low- and intermediate-risk prostate cancer patients that received brachytherapy [low-dose-rate brachytherapy (LDR-BT) or high-dose-rate brachytherapy with external beam radiation therapy (HDR-BT+EBRT)] versus external beam radiation therapy (EBRT) alone. Materials/Methods: Patient data was obtained from the ProCaRS database, which contains 7974 prostate cancer patients treated at four Canadian institutions. Propensity score (PS) matching was used to generate matched cohorts with balanced baseline prognostic factors. Results/Conclusions: Final PS matches included two 1:1 intermediate-risk patient matches, LDR-BT vs. EBRT (total n = 254) and HDR-BT+EBRT vs. EBRT (total n=388), and a 4:1 (LDR-BT:EBRT) low-risk match (total n=400). Hazard ratios for OS were 0.79 (p=0.69), 0.64 (p=0.47), and 1.41 (p=0.50), respectively. Hazard ratios for bFFS were 0.22 (p=0.001), 0.48 (p=0.007), and 0.35 (p=0.004), respectively. Conclusions: PS matching showed BT significantly improved bFFS but not OS in matched prostate cancer patients

Gene expression of PMP22 is an independent prognostic factor for disease-free and overall survival in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Gene expression of peripheral myelin protein 22 (<it>PMP22</it>) and the epithelial membrane proteins (<it>EMPs</it>) was found to be differentially expressed in invasive and non-invasive breast cell lines in a previous study. We want to evaluate the prognostic impact of the expression of these genes on breast cancer.</p> <p>Methods</p> <p>In a retrospective multicenter study, gene expression of <it>PMP22 </it>and the <it>EMPs </it>was measured in 249 primary breast tumors by real-time PCR. Results were statistically analyzed together with clinical data.</p> <p>Results</p> <p>In univariable Cox regression analyses PMP22 and the EMPs were not associated with disease-free survival or tumor-related mortality. However, multivariable Cox regression revealed that patients with higher than median <it>PMP22 </it>gene expression have a 3.47 times higher risk to die of cancer compared to patients with equal values on clinical covariables but lower <it>PMP22 </it>expression. They also have a 1.77 times higher risk to relapse than those with lower <it>PMP22 </it>expression. The proportion of explained variation in overall survival due to <it>PMP22 </it>gene expression was 6.5% and thus PMP22 contributes equally to prognosis of overall survival as nodal status and estrogen receptor status. Cross validation demonstrates that 5-years survival rates can be refined by incorporating <it>PMP22 </it>into the prediction model.</p> <p>Conclusions</p> <p><it>PMP22 </it>gene expression is a novel independent prognostic factor for disease-free survival and overall survival for breast cancer patients. Including it into a model with established prognostic factors will increase the accuracy of prognosis.</p

Presence of symptom clusters in surgically treated patients with esophageal cancer

BACKGROUND: It is not known whether symptoms cluster together after esophageal cancer surgery or whether such symptom clusters are associated with survival in patients with esophageal cancer who are treated surgically. METHODS: Data from a prospective Swedish nationwide cohort study of surgically treated patients with esophageal cancer recruited between 2001 and 2005 were used. General and esophageal cancer-specific symptoms were assessed using the European Organization for Research and Treatment of Cancer QLQ-C30 quality of life questionnaire and the QLQ-OES18 module at 6 months after surgery. Associations between symptom clusters and survival were analyzed using Cox proportional hazards models, providing hazards ratios with 95% confidence intervals, adjusted for other known prognostic factors. RESULTS: Among 402 patients reporting symptoms 6 months after surgery, 3 symptom clusters were identified. The first symptom cluster (“fatigue/pain”) was characterized by symptoms of pain, fatigue, insomnia, and dyspnea and was present in 30% of patients. The second symptom cluster (“reflux/cough”) was characterized by symptoms of dry mouth, problems with taste, coughing, and reflux and was present in 27% of patients. The third symptom cluster (“eating difficulties”) was characterized by appetite loss, dysphagia, eating difficulties, and nausea/vomiting and was present in 28% of patients. The presence of the reflux/cough and eating difficulties symptom clusters was associated with a statistically significantly increased risk of mortality (adjusted hazards ratio, 1.43 [95% confidence interval, 1.08-1.89] and adjusted HR, 1.41 [95% confidence interval, 1.06-1.87], respectively). CONCLUSIONS: Symptoms experienced by surgically treated patients with esophageal cancer appear to cluster together, and the presence of these symptom clusters appears to have strong prognostic value.Vetenskapsrådet, Cancerfonden, Radiumhemmets forskningsfonderPublishe

Predicting Breast Cancer Survivability

this study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on