Prediction of critical illness in elderly outpatients using elder risk assessment: a population-based study

From bedside-to-model: Designing clinical prediction rules for implementation

This editorial comments on the article by Herasevich et al.

Early Prevention of Critical Illness in Older Adults: Adaptation and Pilot Testing of an Electronic Risk Score and Checklist

OBJECTIVE

Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment-ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting.

METHODS

The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction.

RESULTS

Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients.

CONCLUSIONS

This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use.

RECOMMENDATIONS

We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Administrative Data Is Insufficient to Identify Near-Future Critical Illness: A Population-Based Retrospective Cohort Study

Background

Prediction of future critical illness could render it practical to test interventions seeking to avoid or delay the coming event.

Objective

Identify adults having >33% probability of near-future critical illness.

Research Design

Retrospective cohort study, 2013-2015.

Subjects

Community-dwelling residents of Manitoba, Canada, aged 40-89 years.

Measures

The outcome was a near-future critical illness, defined as intensive care unit admission with invasive mechanical ventilation, or non-palliative death occurring 30-180 days after 1 April each year. By dividing the data into training and test cohorts, a Classification and Regression Tree analysis was used to identify subgroups with ≥33% probability of the outcome. We considered 72 predictors including sociodemographics, chronic conditions, frailty, and health care utilization. Sensitivity analysis used logistic regression methods.

Results

Approximately 0.38% of each yearly cohort experienced near-future critical illness. The optimal Tree identified 2,644 mutually exclusive subgroups. Socioeconomic status was the most influential variable, followed by nursing home residency and frailty; age was sixth. In the training data, the model performed well; 41 subgroups containing 493 subjects had ≥33% members who developed the outcome. However, in the test data, those subgroups contained 429 individuals, with 20 (4.7%) experiencing the outcome, which comprised 0.98% of all subjects with the outcome. While logistic regression showed less model overfitting, it likewise failed to achieve the stated objective.

Conclusions

High-fidelity prediction of near-future critical illness among community-dwelling adults was not successful using population-based administrative data. Additional research is needed to ascertain whether the inclusion of additional types of data can achieve this goal.

Systematic review on the instruments used for measuring the association of the level of multimorbidity and clinically important outcomes

OBJECTIVES

There are multiple instruments for measuring multimorbidity. The main objective of this systematic review was to provide a list of instruments that are suitable for use in studies aiming to measure the association of a specific outcome with different levels of multimorbidity as the main independent variable in community-dwelling individuals. The secondary objective was to provide details of the requirements, strengths and limitations of these instruments, and the chosen outcomes.

METHODS

We conducted the review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO registration number: CRD42018105297). We searched MEDLINE, Embase and CINAHL electronic databases published in English and manually searched the Journal of Comorbidity between 1 January 2010 and 23 October 2020 inclusive. Studies also had to select adult patients from primary care or general population and had at least one specified outcome variable. Two authors screened the titles, abstracts and full texts independently. Disagreements were resolved with a third author. The modified Newcastle-Ottawa Scale was used for quality assessment.

RESULTS

Ninety-six studies were identified, with 69 of them rated to have a low risk of bias. In total, 33 unique instruments were described. Disease Count and weighted indices like Charlson Comorbidity Index were commonly used. Other approaches included pharmaceutical-based instruments. Disease Count was the common instrument used for measuring all three essential core outcomes of multimorbidity research: mortality, mental health and quality of life. There was a rise in the development of novel weighted indices by using prognostic models. The data obtained for measuring multimorbidity were from sources including medical records, patient self-reports and large administrative databases.

CONCLUSIONS

We listed the details of 33 instruments for measuring the level of multimorbidity as a resource for investigators interested in the measurement of multimorbidity for its association with or prediction of a specific outcome.

Care Transitions Program for High-Risk Frail Older Adults is Most Beneficial for Patients with Cognitive Impairment

BACKGROUND

Although posthospitalization care transitions programs (CTP) are highly diverse, their overall program thoroughness is most predictive of their success.

OBJECTIVE

To identify components of a successful homebased CTP and patient characteristics that are most predictive of reduced 30-day readmissions.

DESIGN

Retrospective cohort.

PATIENTS

A total of 315 community-dwelling, hospitalized, older adults (≥60 years) at high risk for readmission (Elder Risk Assessment score ≥16), discharged home over the period of January 1, 2011 to June 30, 2013.

SETTING

Midwest primary care practice in an integrated health system.

INTERVENTION

Enrollment in a CTP during acute hospitalization.

MEASUREMENTS

The primary outcome was all-cause readmission within 30 days of the first CTP evaluation. Logistic regression was used to examine independent variables, including patient demographics, comorbidities, number of medications, completion, and timing of program fidelity measures, and prior utilization of healthcare.

RESULTS

The overall 30-day readmission rate was 17.1%. The intensity of follow-up varied among patients, with 17.1% and 50.8% of the patients requiring one and ≥3 home visits, respectively, within 30 days. More than half (54.6%) required visits beyond 30 days. Compared with patients who were not readmitted, readmitted patients were less likely to exhibit cognitive impairment (29.6% vs 46.0%; P = .03) and were more likely to have high medication use (59.3% vs 44.4%; P = .047), more emergency department (ED; 0.8 vs 0.4; P = .03) and primary care visits (4.0 vs 3.0; P = .018), and longer cumulative time in the hospital (4.6 vs 2.5 days; P = .03) within 180 days of the index hospitalization. Multivariable analysis indicated that only cognitive impairment and previous ED visits were important predictors of readmission.

CONCLUSIONS

No single CTP component reliably predicted reduced readmission risk. Patients with cognitive impairment and polypharmacy derived the most benefit from the program.

Does Calculated Prognostic Estimation Lead to Different Outcomes Compared With Experience-Based Prognostication in the ICU? A Systematic Review

Little is known about the impact of providing calculator/guideline based versus clinical experiential-based prognostic estimates to patients/caregivers in the ICU. We sought to determine whether studies have compared types of prognostic estimation in the ICU and associations with outcomes.

Data Sources

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, databases searched were PubMed, Embase, Web of Science, and Cochrane Library. The search was run on January 4, 2016, and April 12, 2017. References for included articles were searched.

Study Selection

Studies meeting the following criteria were included in the analysis: communication of prognostic estimates, a comparator group, and in the adult ICU setting.

Data Extraction

Titles/abstracts were reviewed by two researchers. We identified 10,704 articles of which 10 met inclusion criteria. Seven of the studies included estimates obtained from calculators/guidelines and three were based on subjective estimation wherein clinicians were asked to estimate prognosis based on experience. Only the seven using calculated/guideline based estimation were used for pooled analysis. Of these, one was a randomized trial, and six were nonrandomized before/after studies. All of the studies communicated the calculated/guideline-based estimates to the clinician. Two studies involved the communication of calculated prognostic estimates to the ICU physicians for all ICU patients. Four included identification of high-risk patients based on guidelines or review of historical local data which triggered a palliative care/ethics consultation, and one study included communication to physicians about guideline based likely outcomes for neurologic recovery for patients with out-of-hospital cardiac arrest survivors. The comparator arm in all studies was usual care without protocolized prognostication.

Data Synthesis

Included studies were assessed for risk of bias. The most common outcomes measured were hospital mortality; do-not-resuscitate status; and medical ICU length of stay. In pooled analyses, there was an association between calculated/guideline based prognostic estimation and decreased medical ICU length of stay as well as increased do-not-resuscitate status, but no difference in hospital mortality.

Conclusions

Protocolized assessment of calculator/guideline based prognosis in ICU patients is associated with decreased medical ICU length of stay and increased do-not-resuscitate status but does not have a significant effect on mortality. Future studies should explore how communicating these estimates to physicians changes behaviors including communication to patients/families and whether calculator/guideline based prognostication is associated with improved patient and family rated outcomes.