MONITOR-IC: Determining and Improving Long-term Consequences of ICU Care

Overview

Due to advances in critical care medicine, more patients survive their critical illness. However, intensive care unit (ICU) survivors often experience long-term physical, cognitive and mental problems, summarized as post intensive care syndrome (PICS), impacting their health related quality of life (HRQoL). The aims of this study are to study the: 1) long-term outcomes, 2) predictors for PICS, 3) prediction of long-term HRQoL, 4) ratio between HRQoL of ICU-survivors and healthcare related costs, and 5) effects on the long-term of interventions

Specific research questions: 1. What are the post-intensive care symptoms that patients experience after their ICU admissions and what is their HRQoL? 2. What are important predictors for the various physical, cognitive and mental long- term outcomes and health related quality of live outcome, using traditional statistical techniques and sophisticated AI/ML techniques? 3. What is the ratio between HRQoL and healthcare related costs? 4. What are experiences of patients regarding their problems and quality of life after the ICU? 5. What are the effects of interventions aimed to improve long-term outcomes of ICU patients? Design: The MONITOR-IC study is an ongoing multicenter prospective open cohort study in which long-term outcomes of ICU patients are studied for a period of two to five years. The study is carried out in several ICUs in the Netherlands; including academic hospitals, and non-academic hospitals. ICU patients are recruited from July 2016 onwards and followed for two to five years. Patient recruitment: Patients scheduled for ICU admission after elective surgery, will be recruited at the outpatient clinic (anaesthesiology or cardiac surgery) (Figure 1). Patients with a non-scheduled admission will be recruited at the ICU. Patients receive information by ICU researchers/nurses/intensivists regarding the aim, content and relevance of the MONITOR-IC, and will be asked for participation. Informed consent is asked for the questionnaires, data from the patients' individual medical record and data from their health insurance company. In case patients are unable to give consent, their legal representative will be asked. Methods: Since this is an ongoing open cohort study with multiple questions, several methods will be used including quantitative, qualitatitative and mixed methods. All patients, or their relatives in case patients are not able to fill in the questionnaire themselves, will be approached to complete the self-administered online questionnaire or on patients' request a paper based: at ICU admission, and several times post-ICU 3 - 12 and 24 months; patients included between 2016 and 2022, also receive the questionnaire 60 months post-ICU. To get insight into the situation before the ICU admission, the baseline questionnaire is provided when the patients are asked for informed consent. This could be preoperatively for the planned admissions or after admission at the ICU. Then, patients are asked to rate their situation before the ICU admission. Items in questionnaire: * Patients' health status and HRQoL = SF-12 and the EQ-5D-5L. * Patients' level of frailty and vulnerability = Clinical Frailty Score (CFS) * The level of fatigue = CIS-8, a subscale of the Checklist Individual Strength * Cognitive impairment = abbreviated 14-item Cognitive Failure Questionnaire (CFQ-14). * Anxiety and depression = Hospital Anxiety and Depression Scale (HADS) * Subjective distress, caused by traumatic events = Impact of Event Scale 6-questions (IES-6) * Care needs and support from professionals and informal caregiver * Social consequences = question set for changes in family circumstances, socio-economic stability and care requirements. Medical data Patients' demographics and information regarding their diagnosis and treatment, such as primary conditions, pre-existing co-morbidity, disease severity, (re)admission, length of ICU stay, expected mortality (based on the APACHE III-IV models) and medication, will be extracted from their medical record and the Dutch National Intensive Care Evaluation (NICE) registry. Health Insurance data Healthcare use and related costs, covered by the Dutch healthcare insurance, will be retrieved from Vektis; a Dutch organization which collects and manage health insurance claimed data of all health insurance companies in the Netherlands.This data is collected based on the Diagnosis Treatment Combination (DTC); a total set of activities carried out by the hospital and medical specialists. Additionally, data is collected regarding nursing days, visits at the outpatient clinic and emergency department, nursing homes, ambulance transport, consultation with general practitioner, paramedical care (including physiotherapist, occupational therapist, dietician and speech therapist), prescribed medication, mental healthcare and revalidation. The Vektis database contains data from all for healthcare insured citizens and covers 99% of the total Dutch population. Using patient's unique insurance number the investigators are able to merge patient's insurance data with the questionnaire data and medical data from the medical record at patient level. Care delivered by community nurses and informal caregivers is not included in the Vektis database and will be studied via the questionnaire. Interviews To get insight into the patients' experiences post ICU face to face or in case this is not possible, video supported, semi-structured interviews will be conducted with ICU survivors. Interviews will take place at the participants' preferred location (home or clinic, or via video contact). Patients will be purposively sampled based on various experienced outcomes, such as the quality of life, daily functioning, anxiety, depression, and their experienced needs for more information or emotional support; depending on the research question. Experienced and trained researchers will conduct the interviews. All interviews will be audio recorded and transcribed verbatim. Analysis Data are been checked on a regular basis to identify out-of-range answers, inconsistent responses, and missing data. Data from the questionnaires, medical record and healthcare insurance data will be merged at patient level. Descriptive statistics will be used to describe baseline characteristics. Depending on the research question, different statististical techniques, including traditional statistical tests, and artificial intelligence/machine learning or deep learning techniques will be used. Subgroups will be identified; supervised (based on their illness and condition e.g. sepsis, delirium, co morbidities, ARDS, length of ICU stay, duration of mechanical ventilation, dialysis, social demographics as age, gender, education, family setting etc) and unsupervised. Software Package for the Social Sciences (SPSS) or open source R packages will be used for data analysis and for analyzing the interviews Atlas.ti, will be used.