Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning.

BACKGROUND: Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission. OBJECTIVE: To develop and validate a complication- and treatment-aware outcome prediction tool in aSAH. METHODS: This cohort study included data from an ongoing prospective nationwide multicenter registry on all aSAH patients in Switzerland (Swiss SOS [Swiss Study on aSAH]; 2009-2015). We trained supervised machine learning algorithms to predict a binary outcome at discharge (modified Rankin scale [mRS] ≤ 3: favorable; mRS 4-6: unfavorable). Clinical and radiological variables on admission ("Early" Model) as well as additional variables regarding secondary complications and disease management ("Late" Model) were used. Performance of both models was assessed by classification performance metrics on an out-of-sample test dataset. RESULTS: Favorable functional outcome at discharge was observed in 1156 (62.0%) of 1866 patients. Both models scored a high accuracy of 75% to 76% on the test set. The "Late" outcome model outperformed the "Early" model with an area under the receiver operator characteristics curve (AUC) of 0.85 vs 0.79, corresponding to a specificity of 0.81 vs 0.70 and a sensitivity of 0.71 vs 0.79, respectively. CONCLUSION: Both machine learning models show good discrimination and calibration confirmed on application to an internal test dataset of patients with a wide range of disease severity treated in different institutions within a nationwide registry. Our study indicates that the inclusion of variables reflecting the clinical course of the patient may lead to outcome predictions with superior predictive power compared to a model based on admission data only.

Intraocular Lens Use in an Astronaut During Long Duration Spaceflight.

BACKGROUND: The purpose of this paper is to report the first use of an intraocular lens (IOL) in an astronaut during long duration spaceflight (LDSF). An astronaut developed a unilateral cataract and underwent phacoemulsification with insertion of an acrylic IOL. Approximately 15 mo later he flew on a Soyuz spacecraft to the International Space Station (ISS), where he successfully completed a 6-mo mission. CASE REPORT: Ocular examination, including ultrasound (US), was performed before, during, and after his mission and he was questioned regarding visual changes during each portion of his flight. DISCUSSION: We documented no change in IOL position during his space mission. This astronaut reported excellent and stable vision during liftoff, entry into microgravity (MG), 6 mo on the ISS, descent, and landing. Our results suggest that modern IOLs are stable, effective, and well tolerated during LDSF.Mader TH, Gibson CR, Schmid JF, Lipsky W, Sargsyan AE, Garcia K, Williams JN. Intraocular lens use in an astronaut during long duration spaceflight. Aerosp Med Hum Perform. 2018; 89(1):63-65.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission.CONCLUSIONSThe multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Calcifying nanoparticles (nanobacteria): an additional potential factor for urolithiasis in space flight crews.

Spaceflight-induced microgravity appears to be a risk factor for the development of urinary calculi, resulting in urolithiasis during and after spaceflight. Calcifying nanoparticles, or nanobacteria, multiply more rapidly in simulated microgravity and create external shells of calcium phosphate. The question arises whether calcifying nanoparticles are nidi for calculi and contribute to the development of clinically significant urolithiasis in those who are predisposed to the development of urinary calculi because of intrinsic or extrinsic factors. This case report describes a calculus recovered after flight from an astronaut that, on morphologic and immunochemical analysis (including specific monoclonal antibody staining), demonstrated characteristics of calcifying nanoparticles.