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.

Validation of the PreOperative Score to predict Post-Operative Mortality (POSPOM) in Dutch non-cardiac surgery patients.

BACKGROUND: Standardized risk assessment tools can be used to identify patients at higher risk for postoperative complications and death. In this study, we validate the PreOperative Score to predict Post-Operative Mortality (POSPOM) for in-hospital mortality in a large cohort of non-cardiac surgery patients. In addition, the performance of POSPOM to predict postoperative complications was studied. METHODS: Data from the control cohort of the TRACE (routine posTsuRgical Anesthesia visit to improve patient outComE) study was analysed. POSPOM scores for each patient were calculated post-hoc. Observed in-hospital mortality was compared with predicted mortality according to POSPOM. Discrimination was assessed by receiver operating characteristic curves with C-statistics for in-hospital mortality and postoperative complications. To describe the performance of POSPOM sensitivity, specificity, negative predictive values, and positive predictive values were calculated. For in-hospital mortality, calibration was assessed by a calibration plot. RESULTS: In 2490 patients, the observed in-hospital mortality was 0.5%, compared to 1.3% as predicted by POSPOM. 27.1% of patients had at least one postoperative complication of which 22.4% had a major complication. For in-hospital mortality, POSPOM showed strong discrimination with a C-statistic of 0.86 (95% CI, 0.78-0.93). For the prediction of complications, the discrimination was poor to fair depending on the severity of the complication. The calibration plot showed poor calibration of POSPOM with an overestimation of in-hospital mortality. CONCLUSION: Despite the strong discriminatory performance, POSPOM showed poor calibration with an overestimation of in-hospital mortality. Performance of POSPOM for the prediction of any postoperative complication was poor but improved according to severity.

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.

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Heart Failure in Adult Patients with Congenital Heart Disease.

Adult patients with congenital heart disease are a complex population with a variety of pathophysiologic conditions based on the anatomy and type of surgery or intervention performed, usually during the first years of life. Nowadays, the majority of patients survive childhood and present for a number of noncardiac surgeries or interventions needing appropriate perioperative management. Heart failure is a major contributing factor to perioperative morbidity and mortality. In this review, we present an overview of the most common types of adult patients with congenital heart disease and actual knowledge on therapy and specific risks in this challenging patient population.

TRACE (Routine posTsuRgical Anesthesia visit to improve patient outComE): a prospective, multicenter, stepped-wedge, cluster-randomized interventional study.

BACKGROUND: Perioperative complications occur in 30-40% of non-cardiac surgical patients and are the leading cause of early postoperative morbidity and mortality. Regular visits by trained health professionals may decrease the incidence of complications and mortality through earlier detection and adequate treatment of complications. Until now, no studies have been performed on the impact of routine postsurgical anesthesia visits on the incidence of postoperative complications and mortality. METHODS: TRACE is a prospective, multicenter, stepped-wedge cluster randomized interventional study in academic and peripheral hospitals in the Netherlands. All hospitals start simultaneously with a control phase in which standard care is provided. Sequentially, in a randomized order, hospitals cross over to the intervention phase in which patients at risk are routinely followed up by an anesthesia professional at postoperative days 1 and 3, aiming to detect and prevent or treat postoperative complications. We aim to include 5600 adult patients who are at high risk of developing complications. The primary outcome variable is 30-day postoperative mortality. Secondary outcomes include incidence of postoperative complications and postoperative quality of life up to one year following surgery. Statistical analyses will be performed to compare the control and intervention cohorts with multilevel linear and logistic regression models, adjusted for temporal trends and for clusters (hospitals). The time horizon of the economic (cost-effectiveness) evaluation will be 30 days and one year following surgery. DISCUSSION: TRACE is the first to study the effects of a routine postoperative visit by an anesthesia healthcare professional on mortality and cost-effectiveness of surgical patients. If the intervention proves to be beneficial for the patient and cost-effective, the stepped-wedge design ensures direct implementation in the participating hospitals. TRIAL REGISTRATION: Nederlands Trial Register/Netherlands Trial Registration, NTR5506 . Registered on 02 December 2015.

The patient with chronic heart failure undergoing surgery.

PURPOSE OF REVIEW: Congestive heart failure (CHF) is one of the most common diseases of the elderly patient. The underlying pathophysiology varies considerably and approximately 50% of the patients suffer from CHF with reduced left ventricular ejection fraction. Mortality in the perioperative period is increased in patients with CHF and this holds true for both minor and major surgeries. This review will summarize recent literature in the field of CHF and perioperative outcome in patients undergoing surgery with a special emphasis on actual guidelines, preoperative assessment and appropriate perioperative therapy. RECENT FINDINGS: In the past 18 months, new insights in the short and long-term effects of CHF in the perioperative period have been published. The role of left ventricular ejection fraction has been studied in noncardiac surgical patients and it has been demonstrated that an ejection fraction less than 30% is associated with a significant increase in mortality and myocardial infarctions. Moreover, in 25% of patients, acute exacerbation of heart failure takes place in the perioperative period. The European Society of Anesthesiology published new guidelines on the preoperative evaluation of patients with CHF. The role of adequate preoperative evaluation and preparation of patients with CHF is discussed widely. It becomes clear that parameters like brain natriuretic peptide play a crucial role in risk stratification and prediction of outcome. Also, the treatment of patients with low cardiac output was a topic, and it became clear that established therapies including the use of ß-mimetics and PDE-III inhibitors should only be initiated in very selected patient groups. However, adequately powered studies in patients with CHF are still missing and the majority of knowledge is based on patient undergoing cardiac surgery. SUMMARY: CHF is a source of considerable perioperative morbidity and mortality and in contrast to coronary artery disease, knowledge is very limited and additional research urgently needed.

Platelet monitoring follow-up in a pregnant patient with HELLP syndrome.

Monitoring the course of platelet function in HELLP (haemolysis, elevated liver-enzymes and low platelets) syndrome is important for clinical decision-making. We present a primigravid woman developing HELLP syndrome at 29 weeks and 6 days. Platelet function was monitored by multiple electrode aggregometry (MEA), platelet function analyzer (PFA-100®), platelet count and mean platelet volume (MPV) over an 11-day period. MPV and PFA-100® seem better predictors for platelet function than platelet levels.