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

Determinants of Subjective Mental and Functional Health of Critical Illness Survivors: Comparing Pre-ICU and Post-ICU Status

OBJECTIVES

To compare ICU survivors' subjective mental and functional health before ICU admission and after discharge and to assess determinants of subjective health decline or improvement.

DESIGN

Secondary analysis of the multicenter cluster-randomized Enhanced Recovery after Intensive Care trial ( ClinicalTrials.gov : NCT03671447).

SETTING

Ten ICU clusters in Germany.

PATIENTS

Eight hundred fifty-five patients with 1478 follow-up assessments.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

At two patient follow-ups scheduled 3 and 6 months after ICU discharge, patients rated their subjective mental and functional/physical health on two separate visual analog scales from 0 (worst) to 10 (best) in the previous week and before ICU admission. We compared pre-ICU and post-ICU subjective health and used mixed-effects regression to assess determinants of a health decline or improvement. At the first follow-up, 20% ( n = 165/841) and 30% ( n = 256/849) of patients reported a decline in subjective mental and functional health of at least three points, respectively; 16% ( n = 133/841 and n = 137/849) outlined improvements of mental and functional health. For 65% ( n = 543/841) and 54% ( n = 456/849), mental and functional health did not change three points or more at the first follow-up. Multivariable mixed-effects logistic regressions revealed that the ICU length of stay was a predictor of mental (adjusted odds ratio [OR] per ICU day, 1.04; 95% CI, 1.00-1.09; p = 0.038) and functional health (adjusted OR per ICU day, 1.06; 95% CI, 1.01-1.12; p = 0.026) decline. The odds of a mental health decline decreased with age (adjusted OR per year, 0.98; 95% CI, 0.96-0.99; p = 0.003) and the odds of a functional health decline decreased with time after discharge (adjusted OR per month, 0.86; 95% CI, 0.79-0.94; p = 0.001).

CONCLUSIONS

The majority of ICU survivors did not experience substantial changes in their subjective health status, but patients with long ICU stays were prone to subjective mental and functional health decline. Hence, post-ICU care in post-ICU clinics could focus on these patients.

Prediction of Long-Term Physical, Mental, and Cognitive Problems Following Critical Illness: Development and External Validation of the PROSPECT Prediction Model

OBJECTIVES

ICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission.

DESIGN

Prospective cohort study in seven Dutch ICUs.

SETTING/PATIENTS

ICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80).

CONCLUSIONS

The developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study

PURPOSE

Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

METHODS

Population-based study of children < 16 years requiring ICU admission in Queensland, Australia, between 1997 and 2019. Failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) assessment during primary and secondary school was the primary outcome. Routine ICU information was used to train machine learning classifiers. Models were trained, validated and tested using stratified nested cross-validation.

RESULTS

13,957 childhood ICU survivors with 37,200 corresponding NAPLAN tests after a median follow-up duration of 6 years were included. 14.7%, 17%, 15.6% and 16.6% failed to meet NMS in school grades 3, 5, 7 and 9. The model demonstrated an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8 (standard deviation SD, 0.01), with 51% specificity to reach 85% sensitivity [relative Area Under the Precision Recall Curve (rel-AUPRC) 3.42, SD 0.06]. Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the predictions. In children with no comorbidities admitted between 2009 and 2019, the model achieved a AUROC of 0.77 (SD 0.03) and a rel-AUPRC of 3.31 (SD 0.42).

CONCLUSIONS

A machine learning model using data available at time of ICU discharge predicted failure to meet minimum educational requirements at school age. Implementation of this prediction tool could assist in prioritizing patients for follow-up and targeting of rehabilitative measures.

Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study

OBJECTIVES

To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU and managing their expectations.

DESIGN

Data from a prospective multicenter cohort study (MONITOR-IC) were used.

SETTING

Seven hospitals in the Netherlands.

PATIENTS

ICU survivors greater than or equal to 16 years old.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1,804 patients included in analysis, 1,057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 53.2%).

CONCLUSIONS

This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.