ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

Development and Validation of an Electronic Postoperative Morbidity Score.

BACKGROUND: Electronic health records are being adopted due to numerous potential benefits. This requires the development of objective metrics to characterize morbidity, comparable to studies performed in centers without an electronic health record. We outline the development of an electronic version of the postoperative morbidity score for integration into our electronic health record. METHODS: Twohundred and three frail patients who underwent elective surgery were reviewed. We retrospectively defined postoperative morbidity score on postoperative day 3. We also recorded potential electronic surrogates for morbidities that could not be easily extracted in an objective format. We compared discriminative capability (area under the receiver operator curve) for patients having prolonged length of stay or complex discharge requirements. RESULTS: One hundred thirty-nine patients (68%) had morbidity in ≥1 postoperative morbidity score domain. Initial electronic surrogates were overly sensitive, identifying 173 patients (84%) as having morbidity. We refined our definitions using backward logistic regression against "gold-standard" postoperative morbidity score. The final electronic postoperative morbidity score differed from the initial version in its definition of cardiac and neurological morbidity. There was no significant difference in the discriminative capability between electronic postoperative morbidity score and postoperative morbidity score for either outcome (area under the receiver operator curve: 0.66 vs 0.66 for complex discharge requirement, area under the receiver operator curve: 0.66 vs 0.67 for a prolonged length of stay; P> .05 for both). Patients with postoperative morbidity score or electronic postoperative morbidity score-defined morbidity on day 3 had increased risk of prolonged length of stay (P < .001 for both). CONCLUSIONS: We present a variant of postoperative morbidity score based on objective electronic metrics. Discriminative performance appeared comparable to gold-standard definitions for discharge outcomes. Electronic postoperative morbidity score may allow characterization of morbidity within our electronic health record, but further study is required to assess external validity

Continuous evolution of the in-plane magnetic anisotropies with thickness in epitaxial Fe films

Copyright © 1996 American Institute of Physics.We have studied the evolution of the magnetic in‐plane anisotropy in epitaxial Fe/GaAs films of both (001) and (110) orientation as a function of the Fe layer thickness using the longitudinal magneto‐optic Kerr effect and Brillouin light scattering. Magnetization curves which are recorded in situ during film growth reveal a continuous change of the net anisotropy axes with increasing film thickness. This behavior can be understood to arise from the combination of a uniaxial and a cubic in‐plane magnetic anisotropy which are both thickness dependent. Structural analysis of the substrate and Fe film surfaces provides insight into the contribution of atomic steps at the interfaces to the magnetic anisotropy. Changing the degree of crystalline order at the Fe–GaAs interface allows us to conclude that the magnetic anisotropies are determined by atomic scale order

Computing the first eigenpair of the p-Laplacian via inverse iteration of sublinear supersolutions

We introduce an iterative method for computing the first eigenpair $(\lambda_{p},e_{p})$ for the $p$-Laplacian operator with homogeneous Dirichlet data as the limit of $(\mu_{q,}u_{q})$ as $q\rightarrow p^{-}$, where $u_{q}$ is the positive solution of the sublinear Lane-Emden equation $-\Delta_{p}u_{q}=\mu_{q}u_{q}^{q-1}$ with same boundary data. The method is shown to work for any smooth, bounded domain. Solutions to the Lane-Emden problem are obtained through inverse iteration of a super-solution which is derived from the solution to the torsional creep problem. Convergence of $u_{q}$ to $e_{p}$ is in the $C^{1}$-norm and the rate of convergence of $\mu_{q}$ to $\lambda_{p}$ is at least $O(p-q)$. Numerical evidence is presented.Comment: Section 5 was rewritten. Jed Brown was added as autho

Prediction of early unplanned intensive care unit readmission in a UK tertiary-care hospital: A cross-sectional machine learning approach

Objectives: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult, and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. Setting: A single academic, tertiary care hospital in the United Kingdom. Participants: A set of 3,326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who: were ≤ 16 years of age; visited intensive care units other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2,018 outcome-labeled episodes remained. Primary and Secondary Outcome Measures: Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge. Results: In ten-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the MIMIC-III database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built SWIFT score (AUROC = 0.6082, SE 0.0249; p = 0.014, pairwise t-test). Conclusions: Despite the inherent difficulties, we demonstrate that a novel ML algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.esearch reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945

Concomitant CIS on TURBT does not impact oncological outcomes in patients treated with neoadjuvant or induction chemotherapy followed by radical cystectomy

© Springer-Verlag GmbH Germany, part of Springer Nature 2018Background: Cisplatin-based neoadjuvant chemotherapy (NAC) for muscle invasive bladder cancer improves all-cause and cancer specific survival. We aimed to evaluate whether the detection of carcinoma in situ (CIS) at the time of initial transurethral resection of bladder tumor (TURBT) has an oncological impact on the response to NAC prior to radical cystectomy. Patients and methods: Patients were identified retrospectively from 19 centers who received at least three cycles of NAC or induction chemotherapy for cT2-T4aN0-3M0 urothelial carcinoma of the bladder followed by radical cystectomy between 2000 and 2013. The primary and secondary outcomes were pathological response and overall survival, respectively. Multivariable analysis was performed to determine the independent predictive value of CIS on these outcomes. Results: Of 1213 patients included in the analysis, 21.8% had concomitant CIS. Baseline clinical and pathologic characteristics of the ‘CIS’ versus ‘no-CIS’ groups were similar. The pathological response did not differ between the two arms when response was defined as pT0N0 (17.9% with CIS vs 21.9% without CIS; p = 0.16) which may indicate that patients with CIS may be less sensitive to NAC or ≤ pT1N0 (42.8% with CIS vs 37.8% without CIS; p = 0.15). On Cox regression model for overall survival for the cN0 cohort, the presence of CIS was not associated with survival (HR 0.86 (95% CI 0.63–1.18; p = 0.35). The presence of LVI (HR 1.41, 95% CI 1.01–1.96; p = 0.04), hydronephrosis (HR 1.63, 95% CI 1.23–2.16; p = 0.001) and use of chemotherapy other than ddMVAC (HR 0.57, 95% CI 0.34–0.94; p = 0.03) were associated with shorter overall survival. For the whole cohort, the presence of CIS was also not associated with survival (HR 1.05 (95% CI 0.82–1.35; p = 0.70). Conclusion: In this multicenter, real-world cohort, CIS status at TURBT did not affect pathologic response to neoadjuvant or induction chemotherapy. This study is limited by its retrospective nature as well as variability in chemotherapy regimens and surveillance regimens.Peer reviewedFinal Accepted Versio

Survey in expert clinicians on validity of automated calculation of optimal cerebral perfusion pressure

BACKGROUND: Optimal cerebral perfusion pressure (CPPopt) targeting in traumatic brain injury (TBI) patients constitutes an active and controversial area of research. It has been suggested that an autoregulation guided CPP therapy may improve TBI outcome. Prerequisites of a CPPopt intervention study would be objective criteria for the CPPopt detection.. This study compared the agreement between automated and visual CPPopt detection. METHODS: Twenty-five clinicians from 18 centres worldwide, familiar with brain monitoring and using dedicated software, reviewed ten 4-hour CPPopt screenshots at 48 hrs after ictus in selected TBI patients. Each screenshot displayed the trends of cerebral perfusion pressure (CPP), intracranial pressure (ICP), cerebrovascular pressure reactivity (PRx) as well as the ‘CPP-optimal’ curve and its associated value (automated CPPopt). The main objective was to evaluate the agreement between expert clinicians as well as the agreement between the clinicians and automated CPPopt. RESULTS: Twenty-two clinicians responded to our call (88%). Three screenshots were judged as ‘CPPopt not determinable’ by > 45% of the clinicians. For the whole group, the consensus between automated CPPopt and clinicians’ visual CPPopt was high. Three clinicians were identified as outliers. All clinicians recommended to modify CPP when patients differed > ± 5 mmHg from their CPPopt. The inter-observer consensus was highest in cases with current CPP below the optimal value. CONCLUSIONS: The overall agreement between automated CPPopt and visual CPPopt identified by autoregulation experts was high, except for those cases when the curve was deemed by the clinicians not reliable enough to yield a trustworthy CPPopt

NMR Studies on Structure and Dynamics of the Monomeric Derivative of BS-RNase: New Insights for 3D Domain Swapping

Three-dimensional domain swapping is a common phenomenon in pancreatic-like ribonucleases. In the aggregated state, these proteins acquire new biological functions, including selective cytotoxicity against tumour cells. RNase A is able to dislocate both N- and C-termini, but usually this process requires denaturing conditions. In contrast, bovine seminal ribonuclease (BS-RNase), which is a homo-dimeric protein sharing 80% of sequence identity with RNase A, occurs natively as a mixture of swapped and unswapped isoforms. The presence of two disulfides bridging the subunits, indeed, ensures a dimeric structure also to the unswapped molecule. In vitro, the two BS-RNase isoforms interconvert under physiological conditions. Since the tendency to swap is often related to the instability of the monomeric proteins, in these paper we have analysed in detail the stability in solution of the monomeric derivative of BS-RNase (mBS) by a combination of NMR studies and Molecular Dynamics Simulations. The refinement of NMR structure and relaxation data indicate a close similarity with RNase A, without any evidence of aggregation or partial opening. The high compactness of mBS structure is confirmed also by H/D exchange, urea denaturation, and TEMPOL mapping of the protein surface. The present extensive structural and dynamic investigation of (monomeric) mBS did not show any experimental evidence that could explain the known differences in swapping between BS-RNase and RNase A. Hence, we conclude that the swapping in BS-RNase must be influenced by the distinct features of the dimers, suggesting a prominent role for the interchain disulfide bridges