The patient perspective on the preoperative colorectal cancer care pathway and preparedness for surgery and postoperative recovery-a qualitative interview study.

BACKGROUND AND OBJECTIVES: This study aimed to explore colorectal cancer (CRC) patients' perspectives and experiences regarding the preoperative surgical care pathway and their subsequent preparedness for surgery and postoperative recovery. METHODS: CRC patients were recruited using purposive sampling and were interviewed three times (preoperatively, and 6 weeks and 3 months postoperatively) using semistructured telephone interviews. Interviews were audiotaped, transcribed verbatim and analysed independently by two researchers using thematic analysis with open coding. RESULTS: Data saturation was achieved after including 18 patients. Preoperative factors that contributed to a feeling of preparedness for surgery and recovery were patient-centred- and professional healthcare organization, sincere and personal guidance, and thorough information provision. Postoperatively, patients with complications or physical complaints experienced unmet information needs regarding the impact of complications and what to expect from postoperative recovery. CONCLUSIONS: The preoperative period is a vital period to prepare patients for surgery and recovery in which patients most value personalized information, personal guidance and professionalism. According to CRC patients, the feeling of preparedness for surgery and recovery can be improved by continually providing dosed information. This information should provide the patient with patient-tailored perspectives regarding the impact of (potential) complications and what to expect during recovery.

Vital Signs Prediction for COVID-19 Patients in ICU.

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.