Optimizing an existing prediction model for quality of life one-year post-intensive care unit: An exploratory analysis.

BACKGROUND: This study aimed to improve the PREPARE model, an existing linear regression prediction model for long-term quality of life (QoL) of intensive care unit (ICU) survivors by incorporating additional ICU data from patients' electronic health record (EHR) and bedside monitors. METHODS: The 1308 adult ICU patients, aged ≥16, admitted between July 2016 and January 2019 were included. Several regression-based machine learning models were fitted on a combination of patient-reported data and expert-selected EHR variables and bedside monitor data to predict change in QoL 1 year after ICU admission. Predictive performance was compared to a five-feature linear regression prediction model using only 24-hour data (R2  = 0.54, mean square error (MSE) = 0.031, mean absolute error (MAE) = 0.128). RESULTS: The 67.9% of the included ICU survivors was male and the median age was 65.0 [IQR: 57.0-71.0]. Median length of stay (LOS) was 1 day [IQR 1.0-2.0]. The incorporation of the additional data pertaining to the entire ICU stay did not improve the predictive performance of the original linear regression model. The best performing machine learning model used seven features (R2  = 0.52, MSE = 0.032, MAE = 0.125). Pre-ICU QoL, the presence of a cerebro vascular accident (CVA) upon admission and the highest temperature measured during the ICU stay were the most important contributors to predictive performance. Pre-ICU QoL's contribution to predictive performance far exceeded that of the other predictors. CONCLUSION: Pre-ICU QoL was by far the most important predictor for change in QoL 1 year after ICU admission. The incorporation of the numerous additional features pertaining to the entire ICU stay did not improve predictive performance although the patients' LOS was relatively short.

A comparison of two approaches for modeling dementia progression in a changing patient context.

OBJECTIVES: To explain the heterogeneity in dementia disease trajectory, we studied the influence of changing patient characteristics on disease course by comparing the association of dementia progression with baseline comorbidity and frailty, and with time-varying comorbidity and frailty. METHODS: We used individual growth models to study baseline and time-varying associations in newly diagnosed dementia patients (n = 331) followed for 3 years. We measured cognition using the Mini-Mental State Examination (MMSE), daily functioning using the Disability Assessment for Dementia (DAD), frailty using the Fried criteria and comorbidity using the Cumulative Illness Rating Scale for Geriatrics (CIRS-G). RESULTS: Although baseline comorbidity and frailty were associated with decreased daily functioning at diagnosis, their effects clearly diminished over time. In contrast, when incorporating comorbidity and frailty as time-varying covariates, comorbidity was associated with lower daily functioning, and frailty with both lower cognition and daily functioning. Being frail was associated with a 0.9-point lower MMSE score (p = 0.03) and a 14.9-point lower DAD score (p < 0.01). A 1-point increase in CIRS-G score was associated with a 1.1-point lower DAD score (p < 0.01). CONCLUSIONS: Time-varying comorbidity and frailty were more consistently associated with dementia disease course than baseline comorbidity and frailty. Therefore, modeling only baseline predictors is insufficient for understanding the course of dementia in a changing patient context.

Using long-term predicted Quality of Life in ICU clinical practice to prepare patients for life post-ICU: A feasibility study.

PURPOSE: To examine the feasibility of using the PREdicting PAtients' long-term outcome for Recovery (PREPARE) prediction model for Quality of Life (QoL) 1 year after ICU admission in ICU practice to prepare expected ICU survivors and their relatives for life post-ICU. MATERIALS AND METHODS: Between June 2020 and February 2021, the predicted change in QoL after 1 year was discussed in 25 family conferences in the ICU. 13 physicians, 10 nurses and 19 patients and/or family members were interviewed to evaluate intervention feasibility in ICU practice. Interviews were analysed qualitatively using thematic coding. RESULTS: Patients' median age was 68.0 years, five patients (20.0%) were female and seven patients (28.0%) died during ICU stay. Generally, study participants thought the intervention, which clarified the concept of QoL through visualization and served as a reminder to discuss QoL and expectations for life post-ICU, had merit. However, some participants, especially physicians, thought the prediction model needed more data on more severely ill ICU patients to curb uncertainty. CONCLUSIONS: Using predicted QoL scores in ICU practice to prepare patients and family members for life after ICU discharge is feasible. After optimising the model and implementation strategy, its effectiveness can be evaluated in a larger trial.

Shared decision-making in the ICU from the perspective of physicians, nurses and patients: a qualitative interview study.

OBJECTIVE: To identify views, experiences and needs for shared decision-making (SDM) in the intensive care unit (ICU) according to ICU physicians, ICU nurses and former ICU patients and their close family members. DESIGN: Qualitative study. SETTING: Two Dutch tertiary centres. PARTICIPANTS: 19 interviews were held with 29 participants: seven with ICU physicians from two tertiary centres, five with ICU nurses from one tertiary centre and nine with former ICU patients, of whom seven brought one or two of their close family members who had been involved in the ICU stay. RESULTS: Three themes, encompassing a total of 16 categories, were identified pertaining to struggles of ICU physicians, needs of former ICU patients and their family members and the preferred role of ICU nurses. The main struggles ICU physicians encountered with SDM include uncertainty about long-term health outcomes, time constraints, feeling pressure because of having final responsibility and a fear of losing control. Former patients and family members mainly expressed aspects they missed, such as not feeling included in ICU treatment decisions and a lack of information about long-term outcomes and recovery. ICU nurses reported mainly opportunities to strengthen their role in incorporating non-medical information in the ICU decision-making process and as liaison between physicians and patients and family. CONCLUSIONS: Interviewed stakeholders reported struggles, needs and an elucidation of their current and preferred role in the SDM process in the ICU. This study signals an essential need for more long-term outcome information, a more informal inclusion of patients and their family members in decision-making processes and a more substantial role for ICU nurses to integrate patients' values and needs in the decision-making process.

Development of a practically usable prediction model for quality of life of ICU survivors: A sub-analysis of the MONITOR-IC prospective cohort study.

PURPOSE: As the goal of ICU treatment is survival in good health, we aimed to develop a prediction model for ICU survivors' change in quality of life (QoL) one year after ICU admission. MATERIALS & METHODS: This is a sub-study of the prospective cohort MONITOR-IC study. Adults admitted ≥12 h to the ICU of a university hospital between July 2016-January 2019 were included. Moribund patients were excluded. Change in QoL one year after ICU admission was quantified using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and Short-Form 36 (SF-36). Multivariable linear regression analysis and best subsets regression analysis (SRA) were used. Models were internally validated by bootstrapping. RESULTS: The PREdicting PAtients' long-term outcome for Recovery (PREPARE) model was developed (n = 1308 ICU survivors). The EQ-5D-models had better predictive performance than the SF-36-models. Explained variance (adjusted R2) of the best model (33 predictors) was 58.0%. SRA reduced the number of predictors to 5 (adjusted R2 = 55.3%, SE = 0.3), including QoL, diagnosis of a Cardiovascular Incident and frailty before admission, sex, and ICU-admission following planned surgery. CONCLUSIONS: Though more long-term data are needed to ascertain model accuracy, in future, the PREPARE model may be used to better inform and prepare patients and their families for ICU recovery.

Retrospective cohort study on factors associated with mortality in high-risk pediatric critical care patients in the Netherlands.

BACKGROUND: High-risk patients in the pediatric intensive care unit (PICU) contribute substantially to PICU-mortality. Complex chronic conditions (CCCs) are associated with death. However, it is unknown whether CCCs also increase mortality in the high-risk PICU-patient. The objective of this study is to determine if CCCs or other factors are associated with mortality in this group. METHODS: Retrospective cohort study from a national PICU-database (2006-2012, n = 30,778). High-risk PICU-patients, defined as patients < 18 years with a predicted mortality risk > 30% according to either the recalibrated Pediatric Risk of Mortality-II (PRISM) or the Paediatric Index of Mortality 2 (PIM2), were included. Patients with a cardiac arrest before PICU-admission were excluded. RESULTS: In total, 492 high-risk PICU patients with mean predicted risk of 24.8% (SD 22.8%) according to recalibrated PIM2 and 40.0% (SD 23.8%) according to recalibrated PRISM were included of which 39.6% died. No association was found between CCCs and non-survival (odds ratio 0.99; 95% CI 0.62-1.59). Higher Glasgow coma scale at PICU admission was associated with lower mortality (odds ratio 0.91; 95% CI 0.87-0.96). CONCLUSIONS: Complex chronic conditions are not associated with mortality in high-risk PICU patients.

Factors Associated With Mortality in Low-Risk Pediatric Critical Care Patients in The Netherlands.

OBJECTIVE: To determine differences between survivors and nonsurvivors and factors associated with mortality in pediatric intensive care patients with low risk of mortality. DESIGN: Retrospective cohort study. SETTING: Patients were selected from a national database including all admissions to the PICUs in The Netherlands between 2006 and 2012. PATIENTS: Patients less than 18 years old admitted to the PICU with a predicted mortality risk lower than 1% according to either the recalibrated Pediatric Risk of Mortality or the Pediatric Index of Mortality 2 were included. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In total, 16,874 low-risk admissions were included of which 86 patients (0.5%) died. Nonsurvivors had more unplanned admissions (74.4% vs 38.5%; p < 0.001), had more complex chronic conditions (76.7% vs 58.8%; p = 0.001), were more often mechanically ventilated (88.1% vs 34.9%; p < 0.001), and had a longer length of stay (median, 11 [interquartile range, 5-32] d vs median, 3 [interquartile range, 2-5] d; p < 0.001) when compared with survivors. Factors significantly associated with mortality were complex chronic conditions (odds ratio, 3.29; 95% CI, 1.97-5.50), unplanned admissions (odds ratio, 5.78; 95% CI, 3.40-9.81), and admissions in spring/summer (odds ratio, 1.67; 95% CI, 1.08-2.58). CONCLUSIONS: Nonsurvivors in the PICU with a low predicted mortality risk have recognizable risk factors including complex chronic condition and unplanned admissions.