A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance.

peer reviewedBACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability. RESULTS: The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Pietrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of -2.5%. CONCLUSIONS: The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function

Hemodynamic effects of lung recruitment maneuvers in acute respiratory distress syndrome

Background: Clinical trials have, so far, failed to establish clear beneficial outcomes of recruitment maneuvers (RMs) on patient mortality in acute respiratory distress syndrome (ARDS), and the effects of RMs on the cardiovascular system remain poorly understood. Methods: A computational model with highly integrated pulmonary and cardiovascular systems was configured to replicate static and dynamic cardio-pulmonary data from clinical trials. Recruitment maneuvers (RMs) were executed in 23 individual in-silico patients with varying levels of ARDS severity and initial cardiac output. Multiple clinical variables were recorded and analyzed, including arterial oxygenation, cardiac output, peripheral oxygen delivery and alveolar strain. Results: The maximal recruitment strategy (MRS) maneuver, which implements gradual increments of positive end expiratory pressure (PEEP) followed by PEEP titration, produced improvements in PF ratio, carbon dioxide elimination and dynamic strain in all 23 in-silico patients considered. Reduced cardiac output in the moderate and mild in silico ARDS patients produced significant drops in oxygen delivery during the RM (average decrease of 423 ml min-1 and 526 ml min-1, respectively). In the in-silico patients with severe ARDS, however, significantly improved gas-exchange led to an average increase of 89 ml min-1 in oxygen delivery during the RM, despite a simultaneous fall in cardiac output of more than 3 l min-1 on average. Post RM increases in oxygen delivery were observed only for the in silico patients with severe ARDS. In patients with high baseline cardiac outputs (>6.5 l min-1), oxygen delivery never fell below 700 ml min-1. Conclusions: Our results support the hypothesis that patients with severe ARDS and significant numbers of alveolar units available for recruitment may benefit more from RMs. Our results also indicate that a higher than normal initial cardiac output may provide protection against the potentially negative effects of high intrathoracic pressures associated with RMs on cardiac function. Results from in silico patients with mild or moderate ARDS suggest that the detrimental effects of RMs on cardiac output can potentially outweigh the positive effects of alveolar recruitment on oxygenation, resulting in overall reductions in tissue oxygen delivery

Blood lactate levels in 31 female dogs with pyometra

<p>Abstract</p> <p>Background</p> <p>Canine pyometra is a life-threatening disease common in countries where spaying of dogs is not routinely performed. The disease is associated with endotoxemia, sepsis, systemic inflammatory response syndrome (SIRS) and a 3–4% mortality rate. Blood lactate analysis is clinically valuable in predicting prognosis and survival, evaluating tissue perfusion and treatment response in human and veterinary critical care settings. The aims of the present study were to investigate 1) the blood lactate levels of female dogs with pyometra by a hand-held analyser and 2) if these levels are related with the clinical status or other biochemical or hematological disorders.</p> <p>Methods</p> <p>In total 31 female dogs with pyometra admitted for surgical ovariohysterectomy and 16 healthy female control dogs were included in the present study. A complete physical examination including SIRS-status determination was performed. Blood samples for lactate concentrations, hematological and biochemical parameters, acid-base and blood gas analysis and other laboratory parameters were collected and subsequently analysed. The diagnosis pyometra was verified with histopathological examination of the uterus and ovaries. Increased hospitalisation length and presence of SIRS were used as indicators of outcome.</p> <p>Results</p> <p>In the pyometra group the median blood lactate level was 1,6 mmol l^-1(range <0.8–2.7 mmol l^-1). In the control group the median lactate level was 1,2 mmol l^-1(range <0.8–2.1 mmol l^-1). Of the 31 bitches 19 (61%) fulfilled 2 or more criteria for SIRS at inclusion, 10 bitches (32%) fulfilled 3 of the SIRS criteria whereas none accomplished more than 3 criteria. Lactate levels did not differ significantly between the pyometra and control group, or between the SIRS positive and SIRS negative dogs with pyometra. Increased lactate concentration (>2.5 mmol l^-1) was demonstrated in one female dog with pyometra (3%), and was not associated with longer hospitalisation or presence of SIRS. Lactate measurement was not indicative of peritonitis. None of the bitches died during or within two months of the hospital stay. The measurements of temperature, heart rate, respiratory rate, percentage bandforms of neutrophilic granulocytes, α₂-globulins, creatinin, pvCO₂, TCO₂and base excess showed significant differences between the SIRS positive and the SIRS negative pyometra cases.</p> <p>Conclusion</p> <p>Increased blood lactate concentrations were demonstrated in 3% (1/31), and SIRS was present in 61% (19/31) of the female dogs with pyometra. Preoperative lactate levels were not related with presence of SIRS or prolonged hospitalisation. Lactate measurement was not indicative of peritonitis. The value of a single and repeated lactate analysis in more severely affected cases remains to be determined.</p

Why Are Clinicians Not Embracing the Results from Pivotal Clinical Trials in Severe Sepsis? A Bayesian Analysis

BACKGROUND: Five pivotal clinical trials (Intensive Insulin Therapy; Recombinant Human Activated Protein C [rhAPC]; Low-Tidal Volume; Low-Dose Steroid; Early Goal-Directed Therapy [EGDT]) demonstrated mortality reduction in patients with severe sepsis and expert guidelines have recommended them to clinical practice. Yet, the adoption of these therapies remains low among clinicians. OBJECTIVES: We selected these five trials and asked: Question 1--What is the current probability that the new therapy is not better than the standard of care in my patient with severe sepsis? Question 2--What is the current probability of reducing the relative risk of death (RRR) of my patient with severe sepsis by meaningful clinical thresholds (RRR >15%; >20%; >25%)? METHODS: Bayesian methodologies were applied to this study. Odds ratio (OR) was considered for Question 1, and RRR was used for Question 2. We constructed prior distributions (enthusiastic; mild, moderate, and severe skeptic) based on various effective sample sizes of other relevant clinical trials (unfavorable evidence). Posterior distributions were calculated by combining the prior distributions and the data from pivotal trials (favorable evidence). MAIN FINDINGS: Answer 1--The analysis based on mild skeptic prior shows beneficial results with the Intensive Insulin, rhAPC, and Low-Tidal Volume trials, but not with the Low-Dose Steroid and EGDT trials. All trials' results become unacceptable by the analyses using moderate or severe skeptic priors. Answer 2--If we aim for a RRR>15%, the mild skeptic analysis shows that the current probability of reducing death by this clinical threshold is 88% for the Intensive Insulin, 62-65% for the Low-Tidal Volume, rhAPC, EGDT trials, and 17% for the Low-Dose Steroid trial. The moderate and severe skeptic analyses show no clinically meaningful reduction in the risk of death for all trials. If we aim for a RRR >20% or >25%, all probabilities of benefits become lower independent of the degree of skepticism. CONCLUSIONS: Our clinical threshold analysis offers a new bedside tool to be directly applied to the care of patients with severe sepsis. Our results demonstrate that the strength of evidence (statistical and clinical) is weak for all trials, particularly for the Low-Dose Steroid and EGDT trials. It is essential to replicate the results of each of these five clinical trials in confirmatory studies if we want to provide patient care based on scientifically sound evidence