Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study

<p>Abstract</p> <p>Background</p> <p>Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.</p> <p>Methods</p> <p>We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.</p> <p>Results</p> <p>Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.</p> <p>Conclusions</p> <p>We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.</p

Sudden cardiac death and pump failure death prediction in chronic heart failure by combining ECG and clinical markers in an integrated risk model

BACKGROUND: Sudden cardiac death (SCD) and pump failure death (PFD) are common endpoints in chronic heart failure (CHF) patients, but prevention strategies are different. Currently used tools to specifically predict these endpoints are limited. We developed risk models to specifically assess SCD and PFD risk in CHF by combining ECG markers and clinical variables. METHODS: The relation of clinical and ECG markers with SCD and PFD risk was assessed in 597 patients enrolled in the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study. ECG indices included: turbulence slope (TS), reflecting autonomic dysfunction; T-wave alternans (TWA), reflecting ventricular repolarization instability; and T-peak-to-end restitution (ΔαTpe) and T-wave morphology restitution (TMR), both reflecting changes in dispersion of repolarization due to heart rate changes. Standard clinical indices were also included. RESULTS: The indices with the greatest SCD prognostic impact were gender, New York Heart Association (NYHA) class, left ventricular ejection fraction, TWA, ΔαTpe and TMR. For PFD, the indices were diabetes, NYHA class, ΔαTpe and TS. Using a model with only clinical variables, the hazard ratios (HRs) for SCD and PFD for patients in the high-risk group (fifth quintile of risk score) with respect to patients in the low-risk group (first and second quintiles of risk score) were both greater than 4. HRs for SCD and PFD increased to 9 and 11 when using a model including only ECG markers, and to 14 and 13, when combining clinical and ECG markers. CONCLUSION: The inclusion of ECG markers capturing complementary pro-arrhythmic and pump failure mechanisms into risk models based only on standard clinical variables substantially improves prediction of SCD and PFD in CHF patients

Iliocaval venous obstruction, cardiac preload reserve, and exercise limitation

Cardiac output during exercise increases by as much as five-fold in the untrained man, and by as much as eight-fold in the elite athlete. Increasing venous return is a critical but much overlooked component of the physiological response to exercise. Cardiac disorders such as constrictive pericarditis, restrictive cardiomyopathy and pulmonary hypertension are recognised to impair preload and cause exercise limitation, however the effects of peripheral venous obstruction on cardiac function have not been well described. This manuscript will discuss how obstruction of the iliocaval venous outflow can lead to impairment in exercise tolerance; how such obstructions may be diagnosed, the potential implications of chronic obstructions on sympathetic nervous system activation, and relevance of venous compression syndromes in heart failure with preserved ejection fraction

Wasting as an independent predictor of mortality in patients with cystic fibrosis

BACKGROUND—Cystic fibrosis (CF) is the most common life threatening autosomal recessive disorder in the white population. Wasting has long been recognised as a poor prognostic marker in CF. Whether it predicts survival independently of lung function and arterial blood gas tensions has not previously been reported.
METHODS—584 patients with CF (261 women) of mean (SD) age 21 (7) years were studied between 1985 and 1996, all of whom were being followed up in a tertiary referral centre. Lung function tests, body weight, arterial blood oxygen (PaO(2)) and carbon dioxide (PaCO(2)) tensions were measured. The weight was calculated as a percentage of the ideal body weight for age, height, and sex.
RESULTS—Forced expiratory volume in one second (FEV(1)) recorded at the start of the study was 1.8 (1.0) l (52 (26)% predicted FEV(1)), PaO(2) 9.8 (1.9) kPa, PaCO(2) 5.0 (0.9) kPa, and % ideal weight 92(18)%. During the follow up period (45 (27) months) 137 patients died (5 year survival 72%, 95% CI 67 to 73). FEV(1), % predicted FEV(1), PaO(2), % ideal weight (all p<0.0001), and PaCO(2) (p=0.04) predicted survival. In multivariate analysis, % predicted FEV(1) (p<0.0001), % ideal weight (p=0.004), and PaCO(2) (p=0.02) were independent predictors of outcome. Patients with >85% ideal body weight had a better prognosis at 5 years (cumulative survival 84%, 95% CI 79 to 89) than those with ⩽85% ideal weight (survival 53%, 95% CI 45 to 62), p<0.0001. Percentage predicted FEV(1) (area under curve 0.83; 95% CI 0.78 to 0.87) and % ideal weight (area under curve 0.74; 95% CI 0.68 to 0.79) were accurate predictors of survival at 5 years follow up (receiver-operating characteristic analysis).
CONCLUSIONS—Body wasting is a significant predictor of survival in patients with CF independent of lung function, arterial blood oxygen and carbon dioxide tensions.