Clinical assessment of deficits after SAH: hasty neurosurgeons and accurate neurologists

For survivors of aneurysmal subarachnoid hemorrhage (SAH), somatic and cognitive deficits can affect long-term outcomes. We were interested in comparing the deficits identified in SAH patients, including cognitive deficits, at discharge by neurosurgeons and deficits identified by neurologists upon admission to the rehabilitation unit on the same day. The assessment of deficits might have an impact on referring patients to rehabilitation. This retrospective study included 494 SAH patients treated between 2005 and 2010. Of these, 50 patients were discharged to an affiliated rehabilitation unit. Deficits were grouped into 18 categories and summarized into three groups: major somatic, minor somatic, and cognitive deficits. Major somatic deficits were identified in 16 and 20 patients (p = 0.53), minor somatic deficits in 16 and 44 (p < 0.0001) patients, and cognitive deficits in 36 and 45 (p < 0.04) patients by neurosurgeons and neurologists, respectively. The absolute number of deficits in daily activities identified by the neurosurgeon and neurologist were 21 and 31 major somatic deficits (p = 0.2), 18 and 97 minor somatic deficits (p < 0.0001), and 61 and 147 cognitive deficits (p < 0.0001), respectively. Significant differences in assessment of cognitive and minor somatic deficits between neurosurgeons and neurologists exist. Based on these findings, it is evident that for the neurosurgeon, there needs to be an increased awareness of the assessment of cognitive deficits and a more routine interdisciplinary approach, including the use of neuropsychological evaluations, to ensure a better triage of patients to rehabilitation or for discharge home.

More accurate component alignment in navigated total knee arthroplasty has no clinical benefit at 5-year follow-up

Computer navigation in total knee arthroplasty is somewhat controversial. We have previously shown that femoral component positioning is more accurate with computed navigation than with conventional implantation techniques, but the clinical impact of this is unknown. We now report the 5-year outcome of our previously reported 2-year outcome study.

Are there accurate predictors of long-term vital and functional outcomes in cardiac surgical patients requiring prolonged intensive care?

BACKGROUND AND OBJECTIVE: The decision to maintain intensive treatment in cardiac surgical patients with poor initial outcome is mostly based on individual experience. The risk scoring systems used in cardiac surgery have no prognostic value for individuals. This study aims to assess (a) factors possibly related to poor survival and functional outcomes in cardiac surgery patients requiring prolonged (> or = 5 days) intensive care unit (ICU) treatment, (b) conditions in which treatment withdrawal might be justified, and (c) the patient's perception of the benefits and drawbacks of long intensive treatments. METHODS: The computerized data prospectively recorded for every patient in the intensive care unit over a 3-year period were reviewed and analyzed (n=1859). Survival and quality of life (QOL) outcomes were determined in all patients having required > or =5 consecutive days of intensive treatment (n=194/10.4%). Long-term survivors were interviewed at yearly intervals in a standardized manner and quality of life was assessed using the dependency score of Karnofsky. No interventions or treatments were given, withhold, or withdrawn as part of this study. RESULTS: In-hospital, 1-, and 3-year cumulative survival rates reached 91.3%, 85.6%, and 75.1%, respectively. Quality of life assessed 1 year postoperatively by the score of Karnofsky was good in 119/165 patients, fair in 32 and poor in 14. Multivariate logistic regression analysis of 19 potential predictors of poor outcome identified dialysis as the sole factor significantly (p=0.027) - albeit moderately - reducing long-term survival, and sustained neurological deficit as an inconstant predictor of poor functional outcome (p=0.028). One year postoperatively 0.63% of patients still reminded of severe suffering in the intensive station and 20% of discomfort. Only 7.7% of patients would definitely refuse redo surgery. CONCLUSIONS: This study of cardiac surgical patients requiring > or =5 days of intensive treatment did not identify factors unequivocally justifying early treatment limitation in individuals. It found that 1-year mortality and disability rates can be maintained at a low level in this subset of patients, and that severe suffering in the ICU is infrequent.

Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.