Background:Postoperative complications and readmissions to hospital are factors known to negatively influence the short- and long-term quality of life of patients with gastrointestinal cancer. Active family involvement in activities, such as fundamental care activities, has the potential to improve the quality of health care. However, there is a lack of evidence regarding the relationship between active family involvement and outcomes in patients with gastrointestinal cancer after surgery.Objective:This protocol aims to evaluate the effect of a family involvement program (FIP) on unplanned readmissions of adult patients undergoing surgery for malignant gastrointestinal tumors. Furthermore, the study aims to evaluate the effect of the FIP on family caregiver (FC) burden and their well-being and the fidelity of the FIP.Methods:This cohort study will be conducted in 2 academic hospitals in the Netherlands. The FIP will be offered to adult patients and their FCs. Patients are scheduled for oncological gastrointestinal surgery and have an expected hospital stay of at least 5 days after surgery. FCs must be willing to participate in fundamental care activities during hospitalization and after discharge. Consenting patients and their families will choose to either participate in the FIP or be included in the usual care group. According to the power calculation, we will recruit 150 patients and families in the FIP group and 150 in the usual care group. The intervention group will receive the FIP that consists of information, shared goal setting, task-oriented training, participation in fundamental care, presence of FCs during ward rounds, and rooming-in for at least 8 hours a day. Patients in the comparison group will receive usual postoperative care. The primary outcome measure is the number of unplanned readmissions up to 30 days after surgery. Several secondary outcomes will be collected, that is, total number of complications (sensitive to fundamental care activities) at 30 and 90 days after surgery, emergency department visits, intensive care unit admissions up to 30 and 90 days after surgery, hospital length of stay, patients’ quality of life, and the amount of home care needed after discharge. FC outcomes are caregiver burden and well-being up to 90 days after participating in the FIP. To evaluate fidelity, we will check whether the FIP is executed as intended. Univariable regression and multivariable regression analyses will be conducted.Results:The first participant was enrolled in April 2019. The follow-up period of the last participant ended in May 2022. The study was funded by an unrestricted grant of the University hospital in 2018. We aim to publish the results in 2023.Conclusions:This study will provide evidence on outcomes from a FIP and will provide health care professionals practical tools for family involvement in the oncological surgical care setting.
Background While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling. Methods Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (= one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis. Results Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice. Conclusions We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
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Het generalist-plus-specialistische palliatieve zorgmodel wordt wereldwijd onderschreven. In Nederland zijn de competenties en het profiel van de generalistische aanbieder van palliatieve zorg beschreven op alle professionele niveaus in de verpleegkunde en geneeskunde.
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