Care and neurorehabilitation in the disorder of consciousness: a model in progress

Abstract

The operational model and strategies developed at the Institute S. Anna-RAN to be applied in the care and neurorehabilitation of subjects with disorders of consciousness (DOC) are described. The institute units are sequentially organized to guarantee appropriate care and provide rehabilitation programs adapted to the patients' clinical condition and individual's needs at each phase of evolution during treatment in a fast turnover rate. Patients eligible of home care are monitored remotely. Transferring advanced technology to a stage of regular operation is the main mission. Responsiveness and the time windows characterized by better residual responsiveness are identified and the spontaneous/induced changes in the autonomic system functional state and biological parameters are monitored both in dedicated sessions and by means of an ambient intelligence platform acquiring large databases from traditional and innovative sensors and interfaced with knowledge management and knowledge discovery systems. Diagnosis of vegetative state/unresponsive wakefulness syndrome or minimal conscious state and early prognosis are in accordance with the current criteria. Over one thousand patients with DOC have been admitted and treated in the years 1998-2013. The model application has progressively shortened the time of hospitalization and reduced costs at unchanged quality of services.

Figure 1

The S. Anna model for the care and neurorehabilitation of brain-injured subjects with DOC.

Figure 2

Average time (days) spent in the ICU or neurology/neurosurgery units of local hospitals before admission and in the Institute S. Anna-RAN dedicated units after referral.

Figure 3

(a) Probability of observing a positive visual pursuit response over time during the day in VS/UWS and MCS subjects repeatedly tested (6 tests/subject/day). Maxima are at approximately 10.00 a.m. and 3.00 p.m., while no response was observed at postprandial time (after Candelieri et al., 2011 [41], modified). (b) Top right: scatterplot of positive visual pursuit responses (green) and no observed responses (red) in a group of DOC subjects versus the values of the HRV descriptors nuLF and pkLF; middle: support vector machine model predicting the target data values (presence or absence of a pursuit response) to which specific attributes (the HRV descriptors) could be related; bottom left: probability of observing a pursuit response estimated as the relative frequency of response for each subject versus each HRV descriptor (after Riganello et al., 2013 [39], modified). The overall incidence of positive responses or no response at all is summarized in the inset at (c), where the model areas at which a response could or could not be predicted by HRV estimates are in green and red, respectively, and the actual percentage of responses/no responses is reported.

Figure 4

Overall outcome of patients in VS/UWS at admission in the years 1998–2013. The GOS classes are as follows: GOS1 = death; GOS2 = VS/UWS exceeding one year in duration; GOS3 = recovery, with severe disabilities; GOS4 = recovery, with mild disabilities; and GOS5 = full recovery or recovery with minimal disabilities not interfering with the everyday life [23].

Figure 5

General scheme of the ambient intelligence system now operative at the Institute S. Anna-RAN.