Predictive value of a novel digital risk calculator to determine early patient outcomes after major surgery: a proof-of-concept pilot study.

BACKGROUND: A structured risk assessment of patients with validated and evidence-based tools can help to identify modifiable factors before major surgeries. The Protego Maxima trial investigated the value of a new digitized risk assessment tool that combines tools which can be easily used and implemented in the clinical workflow by doctors and qualified medical staff. The hypothesis was that the structured assessment and risk-grouping is predictive of short-term surgical quality reflected by complications and overall survival. METHODS: The Protego Maxima Trial was a prospective cohort analysis of patients undergoing major surgery (visceral, thoracic, urology, vascular and gynecologic surgeries) as key inclusion criterion and the absence of an acute or acute on chronically decompensated pulmo-cardiovascular decompensation. Patients were risk-scored with the software (The Prehab App) that includes a battery of evidence-based risk assessment tools that allow a structured risk assessment. The data were grouped to predefined high and low risk groups and aggregate and individual scores. The primary outcome was to validate the predictive value of the RAI score and the TUG for overall survival in the high and low risk groups. Secondary outcomes were surgical outcomes at 90-days after surgery (overall survival, Clavien-Dindo (CD) 1-5 (all complications), and CD 3-5 (major complications)). The study was carried out in accordance with the DIN ISO 14,155, and the medical device regulation (MDR) at Frankfurt University Hospital between March 2022 and January 2023. RESULTS: In total 267 patients were included in the intention to treat analysis. The mean age was 62.1 ± 12.4 years. Patients with a RAI score > 25 and/or a timed up and go (TUG) > 8 s had a higher risk for mortality at 90 days after surgery. The low-risk group predicted beneficial outcome and the high-risk group predicted adverse outcome in the ROC analysis (Area Under the Curve Receiver Operator Characteristics: AUROC > 0.800; p = 0.01). Risk groups (high vs. low) showed significant differences for 90-day survival (99.4% vs. 95.5%; p = 0.04) and major complications (16.4% vs. 32.4%; p < 0.001). CONCLUSION: The proof-of-concept trial showed that a risk assessment with 'The Prehab App' may be viable to estimate the preoperative risk for mortality and major complications before major surgeries. The overall performance in this initial set of data indicated a certain reliability of the scoring and risk grouping, especially of the RAI score and the TUG. A larger data set will be required to proof the generalizability of the risk scoring to every subgroup and may be fostered by artificial intelligence approaches. TRIAL REGISTRATION: Ethics number: 2021-483-MDR/MPDG-zuständig monocentric; The Federal Institute for Pharmaceuticals and Medical Devices/BfArM, reference number: 94.1.04-5660-13655; Eudamed: CIV-21-07-0307311; German Clinical Trial Registry: DRKS 00026985.

A toolbox for a structured risk-based prehabilitation program in major surgical oncology.

Prehabilitation is a multimodal concept to improve functional capability prior to surgery, so that the patients' resilience is strengthened to withstand any peri- and postoperative comorbidity. It covers physical activities, nutrition, and psychosocial wellbeing. The literature is heterogeneous in outcomes and definitions. In this scoping review, class 1 and 2 evidence was included to identify seven main aspects of prehabilitation for the treatment pathway: (i) risk assessment, (ii) FITT (frequency, interventions, time, type of exercise) principles of prehabilitation exercise, (iii) outcome measures, (iv) nutrition, (v) patient blood management, (vi) mental wellbeing, and (vii) economic potential. Recommendations include the risk of tumor progression due to delay of surgery. Patients undergoing prehabilitation should perceive risk assessment by structured, quantifiable, and validated tools like Risk Analysis Index, Charlson Comorbidity Index (CCI), American Society of Anesthesiology Score, or Eastern Co-operative Oncology Group scoring. Assessments should be repeated to quantify its effects. The most common types of exercise include breathing exercises and moderate- to high-intensity interval protocols. The program should have a duration of 3-6 weeks with 3-4 exercises per week that take 30-60 min. The 6-Minute Walking Testing is a valid and resource-saving tool to assess changes in aerobic capacity. Long-term assessment should include standardized outcome measurements (overall survival, 90-day survival, Dindo-Clavien/CCI®) to monitor the potential of up to 50% less morbidity. Finally, individual cost-revenue assessment can help assess health economics, confirming the hypothetic saving of $8 for treatment for $1 spent for prehabilitation. These recommendations should serve as a toolbox to generate hypotheses, discussion, and systematic approaches to develop clinical prehabilitation standards.

Study protocol for a pilot trial analysing the usability, validity and safety of an interventional health app programme for the structured prehabilitation of patients before major surgical interventions: the PROTEGO MAXIMA trial.

INTRODUCTION: Major surgery is associated with a high risk for postoperative complications, leading to an increase in mortality and morbidity, particularly in frail patients with a reduced cardiopulmonary reserve. Prehabilitation, including aerobic exercise training, aims to improve patients' physical fitness before major surgery and reduce postoperative complications, length of hospital stay and costs. The purpose of the study is to assess the usability, validity and safety of an app-based endurance exercise software in accordance with the Medical Device Regulation using wrist-worn wearables to measure heart rate (HR) and distance. METHODS AND ANALYSIS: The PROTEGO MAXIMA trial is a prospective, interventional study with patients undergoing major elective surgery, comprising three tasks. Tasks I and II aim to assess the usability of the app, using evaluation questionnaires and usability scenarios. In Task IIIa, patients will undergo a structured risk assessment by the Patronus App, which will be correlated with the occurrence of postoperative complications after 90 days (non-interventional). In Task IIIb, healthy students and patients will perform a supervised 6 min walking test and a 37 min interval training on a treadmill based on HR reserve, wearing standard ECG limb leads and two smartwatches, which will be driven by the test software. The aim of this task is to assess the accuracy of HR measurement by the wearables and the safety, using specific alarm settings of the devices and lab testing of the participants (interventional). ETHICS AND DISSEMINATION: Ethical approval was granted by the Institutional Review Board of the University Hospital of Frankfurt and by the Federal Institute for Pharmaceuticals and Medical Products (BfArM, reference number 94.1.04-5660-13655) on 7 February 2022. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings. TRIAL REGISTRATION NUMBERS: European Database on Medical Devices (CIV-21-07-037311) and German Clinical Trial Registry (DRKS00026985).

The Diagnostic Efficacy of an App-based Diagnostic Health Care Application in the Emergency Room: eRadaR-Trial. A prospective, Double-blinded, Observational Study.

OBJECTIVE: To evaluate the diagnostic accuracy of the app-based diagnostic tool Ada and the impact on patient outcome in the emergency room (ER). BACKGROUND: Artificial intelligence-based diagnostic tools can improve targeted processes in health care delivery by integrating patient information with a medical knowledge base and a machine learning system, providing clinicians with differential diagnoses and recommendations. METHODS: Patients presenting to the ER with abdominal pain self-assessed their symptoms using the Ada-App under supervision and were subsequently assessed by the ER physician. Diagnostic accuracy was evaluated by comparing the App-diagnoses with the final discharge diagnoses. Timing of diagnosis and time to treatment were correlated with complications, overall survival, and length of hospital stay. RESULTS: In this prospective, double-blinded study, 450 patients were enrolled and followed up until day 90. Ada suggested the final discharge diagnosis in 52.0% (95% CI [0.47, 0.57]) of patients compared with the classic doctor-patient interaction, which was significantly superior with 80.9% (95% CI [0.77, 0.84], P <0.001). However, when diagnostic accuracy of both were assessed together, Ada significantly increased the accuracy rate (87.3%, P <0.001), when compared with the ER physician alone. Patients with an early time point of diagnosis and rapid treatment allocation exhibited significantly reduced complications ( P< 0.001) and length of hospital stay ( P< 0.001). CONCLUSION: Currently, the classic patient-physician interaction is superior to an AI-based diagnostic tool applied by patients. However, AI tools have the potential to additionally benefit the diagnostic efficacy of clinicians and improve quality of care.