Automated real-time text messaging as a means for rapidly identifying acute stroke patients for clinical trials

Background Recruiting stroke patients into acute treatment trials is challenging because of the urgency of clinical diagnosis, treatment, and trial inclusion. Automated alerts that identify emergency patients promptly may improve trial performance. The main purposes of this project were to develop an automated real-time text messaging system to immediately inform physicians of patients with suspected stroke and to test its feasibility in the emergency setting. Methods An electronic standardized stroke algorithm (SSA) was implemented in the clinical information system (CIS) and linked to a remote data capture system. Within 10 minutes following the documentation and storage of basic information to CIS, a text message was triggered for patients with suspected stroke and sent to a dedicated trial physician. Each text message provided anonymized information on the exact department and unit, date and time of admission, age, sex, and National Institute of Health Stroke Scale (NIHSS) of the patient. All necessary information needed to generate a text message was already available – routine processes in the emergency department were not affected by the automated real-time text messaging system. The system was tested for three 4-week periods. Feasibility was analyzed based on the number of patients correctly identified by the SSA and the door-to-message time. Results In total, 513 text messages were generated for patients with suspected stroke (median age 74 years (19–106); 50.3% female; median NIHSS 4 (0–41)), representing 96.6% of all cases. For 48.3% of these text messages, basic documentation was completed within less than 1 hour and a text message was sent within 60 minutes after patient admission. Conclusions The system proved to be stable in generating text messages using IT-based CIS to identify acute stroke trial patients. The system operated on information which is documented routinely and did not result in a higher workload. Delays between patient admission and the text message were caused by delayed completion of basic documentation. To use the automated real-time text messaging system to immediately identify emergency patients suitable for acute stroke trials, further development needs to focus on eliminating delays in documentation for the SSA in the emergency department

Prospective study on the mismatch concept in acute stroke patients within the first 24 h after symptom onset - 1000Plus study

<p>Abstract</p> <p>Background</p> <p>The mismatch between diffusion weighted imaging (DWI) lesion and perfusion imaging (PI) deficit volumes has been used as a surrogate of ischemic penumbra. This pathophysiology-orientated patient selection criterion for acute stroke treatment may have the potential to replace a fixed time window. Two recent trials - DEFUSE and EPITHET - investigated the mismatch concept in a multicenter prospective approach. Both studies randomized highly selected patients (n = 74/n = 100) and therefore confirmation in a large consecutive cohort is desirable. We here present a single-center approach with a 3T MR tomograph next door to the stroke unit, serving as a bridge from the ER to the stroke unit to screen all TIA and stroke patients. Our primary hypothesis is that the prognostic value of the mismatch concept is depending on the vessel status. Primary endpoint of the study is infarct growth determined by imaging, secondary endpoints are neurological deficit on day 5-7 and functional outcome after 3 months.</p> <p>Methods and design</p> <p>1000Plus is a prospective, single centre observational study with 1200 patients to be recruited. All patients admitted to the ER with the clinical diagnosis of an acute cerebrovascular event within 24 hours after symptom onset are screened. Examinations are performed on day 1, 2 and 5-7 with neurological examination including National Institute of Health Stroke Scale (NIHSS) scoring and stroke MRI including T2*, DWI, TOF-MRA, FLAIR and PI. PI is conducted as dynamic susceptibility-enhanced contrast imaging with a fixed dosage of 5 ml 1 M Gadobutrol. For post-processing of PI, mean transit time (MTT) parametric images are determined by deconvolution of the arterial input function (AIF) which is automatically identified. Lesion volumes and mismatch are measured and calculated by using the perfusion mismatch analyzer (PMA) software from ASIST-Japan. Primary endpoint is the change of infarct size between baseline examination and day 5-7 follow up.</p> <p>Discussions</p> <p>The aim of this study is to describe the incidence of mismatch and the predictive value of PI for final lesion size and functional outcome depending on delay of imaging and vascular recanalization. It is crucial to standardize PI for future randomized clinical trials as for individual therapeutic decisions and we expect to contribute to this challenging task.</p> <p>Trial Registration</p> <p>clinicaltrials.gov NCT00715533</p

Analysis and interpetation of key processes in conducting clinical trials using the example of the Center for Stroke Research Berlin (CSB)

Die Rekrutierungsphase ist ein entscheidender Abschnitt jeder klinischen Prüfung und bestimmt maßgeblich die Dauer einer Studie. Eine realistische Planung dieser zentralen Studienphase muss neben der laut Fallzahlplanung erforderlichen Teilnehmerzahl die erwartete Verfügbarkeit von Patienten bzw. Probanden berücksichtigen. Um die solchermaßen prognostizierten Rekrutierungsraten zu realisieren, ist eine möglichst vollständige Erfassung potentieller Studienteilnehmer in der Rekrutierungsphase von fundamentaler Bedeutung. Ziel der vorliegenden Dissertation ist die Charakterisierung der Zusammenhänge zwischen Selektionskriterien und der Patientenverfügbarkeit bzw. den daraus resultierenden Rekrutierungsraten sowie die Evaluierung von IT- Algorithmen, die das Auffinden potentiell geeigneter Studienteilnehmer unterstützen. Im Projekt BIAS wurden die Ursachen für die unerwarteten Rekrutierungsschwierigkeiten einer am Centrum für Schlaganfallforschung Berlin (CSB) durchgeführten Studie analysiert. Zu diesem Zweck wurden in einem intensivierten Screening die Gründe, die zum Nicht-Einschluss von Schlaganfallpatienten führten, erfasst. In der Studie konnte die Bedeutung des kumulativen Effektes der Selektionskriterien auf die Patientenverfügbarkeit gezeigt werden: unter Berücksichtigung aller Selektionskriterien waren weniger als fünf Prozent der Schlaganfallpatienten für die Studie geeignet. Die Ergebnisse legen nahe, dass Rekrutierungsprognosen, die kumulative Effekte nicht einbeziehen und die Selektionskriterien unabhängig voneinander betrachten, zu unrealistischen Rekrutierungserwartungen führen. Im Projekt STREAM wurde ein Algorithmus evaluiert, der einen zeitnahen, automatisierten SMS-Versand für jeden im Krankenhaus neu aufgenommenen Schlaganfallpatienten an die Studienärztin auslöste. Für den Algorithmus wurden ausschließlich Routinedaten aus dem Krankenhaus-Informationssystem verarbeitet, so dass keine zusätzliche Datenerhebung und –dokumentation notwendig war. In den Projektauswertungen erwies sich der Algorithmus als technisch stabil und gewährleistete eine nahezu vollständige Identifikation aller Schlaganfallpatienten und den anschließenden SMS-Versand. Für den Einsatz im Akutstudienbereich, speziell im Bereich der akuten Schlaganfallstudien, war der Algorithmus aufgrund der zeitlichen Verzögerung des SMS-Versandes von durchschnittlich 62 Minuten nach Aufnahme des Patienten im Krankenhaus nur bedingt geeignet. Die Anwendung des Algorithmus in weniger zeitkritischen Studien erscheint dagegen sinnvoll und machbar. Im Projekt Study Matcher wurde ein IT-Algorithmus untersucht, der aus einer Vielzahl an Schlaganfallstudien die für den jeweiligen Patienten passenden Studien automatisiert identifizierte und eine Studienvorschlagsliste erstellte. Auch dieser Algorithmus nutzte ausschließlich Routinedaten aus dem Krankenhaus- Informationssystem, wobei die Besonderheit darin lag, dass neben strukturierten Daten auch unstrukturiert vorliegende Informationen für die Auswertung aufbereitet und nutzbar gemacht wurden. Es konnte gezeigt werden, dass sich durch den Algorithmus der Aufwand für das manuelle Patientenscreening verringern lässt. Dennoch bleibt das manuelle Screening aufgrund unsicherer Faktoren oder unvollständiger Daten zumindest für den Bereich der Schlaganfallstudien bis auf weiteres unerlässlich.The recruiting phase is a crucial part of a clinical trial und substantially determines the duration of the whole study. A realistic scheduling of this central phase has to take into consideration both the required number of patients as well as the potential availability of participants. The ability to identify all potential study participants is of utmost relevance to fulfil the intended recruiting rates. This thesis analyzed the relation between selection criteria, availability of patients and the resulting recruiting rates and evaluated IT-algorithms that support the identification of potential trial participants. The project BIAS examines a stroke trial that was conducted in the Center for Stroke Research Berlin (CSB) and encountered unexpected recruiting problems. To analyze complications in patient recruitment, an intensified screening was initiated and the reasons for trial non-inclusion were documented for all CSB stroke patients. The project demonstrates the strong cumulative effects of selection criteria, i.e. less than 5% of stroke patients are potential participants when the whole of the selection criteria are taken into account. The results show that recruiting prognoses that ignore possible cumulative effects and regard selection criteria as independent factors will lead to unrealistic expectations. The project STREAM evaluates an IT-algorithm that sent an automated text message to a study physician for every new stroke patient admitted to the hospital. The algorithm operates on routine data from the clinical information system. The analysis shows that the algorithm proved to be technically stable and ensured a nearly complete identification of stroke patients. The algorithm provides only a limited benefit for acute trials, especially acute stroke trials, because of an average delay of 62 minutes between admission to hospital and transmission of text message. The implementation of the algorithm in less time critical trials seems to be feasible. The project Study Matcher investigates an IT-algorithm that provides an automated matching of appropriate trials to a stroke patient based on routinely documented information. The strength of this algorithm was the semantic processing of unstructured data from the clinical information system into data ready for automated utilization. Results show that the manual screening effort for identification of trial participants can be reduced by automatic algorithms. Yet, for stroke studies, additional manual screening remains crucial mostly to account for uncertain or incomplete patient data

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Automated real-time text messaging as a means for rapidly identifying acute stroke patients for clinical trial