Medical futility regarding cardiopulmonary resuscitation in in-hospital cardiac arrests of adult patients: A systematic review and Meta-analysis

Comprehensive geriatric assessment as an essential tool to register or update DNR codes in a tertiary care hospital

PURPOSE

To investigate the prevalence of Do not Resuscitate (DNR) code registration in patients with a geriatric profile admitted to Antwerp University Hospital, a tertiary care hospital in Flanders, Belgium, and the impact of comprehensive geriatric assessment (CGA) on DNR code registration.

PATIENTS AND METHODS

Retrospective analysis of a population of 543 geriatric patients (mean age 82.4 ± 5.19 years, 46.4% males) admitted to Antwerp University Hospital from 2018 to 2020 who underwent a CGA during admission. An association between DNR code registration status before and at hospital admission and age, gender, ethnicity, type of residence, clinical frailty score (CFS), cognitive and oncological status, hospital ward and stay on intensive care was studied. Admissions before and during the first wave of the pandemic were compared.

RESULTS

At the time of hospital admission, a DNR code had been registered for 66.3% (360/543) of patients. Patients with a DNR code at hospital admission were older (82.7 ± 5.5 vs. 81.7 ± 4.6 years, p = 0.031), more frail (CFS 5.11 ± 1.63 vs. 4.70 ± 1.61, p = 0.006) and less likely to be admitted to intensive care. During the hospital stay, the proportion of patients with a DNR code increased to 77% before and to 85.3% after CGA (p < 0.0001). Patients were consulted about and agreed with the registered DNR code in 55.8% and 52.1% of cases, respectively. The proportion of patients with DNR codes at the time of admission or registered after CGA did not differ significantly before and after the start of the COVID-19 pandemic.

CONCLUSION

After CGA, a significant increase in DNR registration was observed in hospitalized patients with a geriatric profile.

Diagnostic accuracy of ePOS score in predicting DNR labeling after ICU admission: A prospective observational study (ePOS-DNR)

Background

Resuscitation can sometimes be futile and making a do-not-resuscitate (DNR) decision is in the best interest of the patient. The electronic poor outcome screening (ePOS) score was developed to predict 6-month poor outcomes of critically ill patients. We explored the diagnostic accuracy of the ePOS score in predicting DNR decisions in the intensive care unit (ICU).

Methods

This study was conducted at the ICU of a tertiary referral hospital in Saudi Arabia between March and May 2023. Prospectively, we calculated ePOS scores for all eligible consecutive admissions after 48 h in the ICU and recorded the DNR orders. The ability of the score to predict DNR was explored using logistic regression. Youden's ideal cut-off value was calculated using the DeLong method, and different diagnostic accuracy measures were generated with corresponding 95 % confidence intervals (CIs).

Results

We enrolled 857 patients, 125 received a DNR order and 732 did not. The average ePOS score of DNR and non-DNR patients was 28.2±10.7 and 15.2±9.7, respectively. ePOS score, as a predictor of DNR order, had an area under receiver operator characteristic (AUROC) curve of 81.8 % (95% CI: 79.0 to 84.3, P <0.001). Youden's ideal cut-off value >17 was associated with a sensitivity of 87.2 (95% CI: 80.0 to 92.5, P <0.001), specificity of 63.9 (95% CI: 60.3 to 67.4, P <0.001), positive predictive value of 29.2 (95% CI: 24.6 to 33.8, P <0.001), negative predictive value of 96.7 (95% CI: 95.1 to 98.3, P <0.001), and diagnostic odds ratio 12.1 (95% CI: 7.0 to 20.8, P <0.001).

Conclusions

In this study, the ePOS score performed well as a diagnostic test for patients who will be labeled as DNR during their ICU stay. A cut-off score >17 may help guide clinical decisions to withhold or commence resuscitative measures.

Introducing "A Question That Might, Perhaps, Scare you": How Geriatric Physicians Approach the Discussion About Cardiopulmonary Resuscitation with Hospitalized Patients

Decisions about the relevance of life-sustaining treatment, such as cardiopulmonary resuscitation (CPR), are commonly made when a patient is admitted to the hospital. This article aims to refine our understanding of how discussions about CPR are introduced, to identify and classify the components frequently occurring in these introductions, and discuss their implications within the overarching activity (discussing CPR). We recorded 43 discussions about CPR between physicians and patients, taking place during the admission interview. We applied an inductive qualitative content analysis and thematic analysis to all the encounter content from the launch of the conversation on CPR to the point at which the physician formulated a question or the patient an answer. We identified this part of the encounter as the "introduction." This systematic method allowed us to code the material, develop and assign themes and subthemes, and quantify it. We identified four major themes in the introductions: (i) agenda setting; (ii) circumstances leading to CPR (subthemes: types of circumstances, personal prognostics of cardiac arrest); (iii) the activity of addressing CPR with the patient (subthemes: routine, constrain, precedence, sensitivity); and (iv) mentioning advance directives. Our findings reveal the elaborate effort that physicians deploy by appealing to combinations of these themes to account for the need to launch conversations about CPR, and highlight how CPR emerges as a sensitive topic.

How to predict the death risk after an in-hospital cardiac arrest (IHCA) in intensive care unit? A retrospective double-centre cohort study from a tertiary hospital in China

OBJECTIVES

Our objective is to develop a prediction tool to predict the death after in-hospital cardiac arrest (IHCA).

DESIGN

We conducted a retrospective double-centre observational study of IHCA patients from January 2015 to December 2021. Data including prearrest diagnosis, clinical features of the IHCA and laboratory results after admission were collected and analysed. Logistic regression analysis was used for multivariate analyses to identify the risk factors for death. A nomogram was formulated and internally evaluated by the boot validation and the area under the curve (AUC). Performance of the nomogram was further accessed by Kaplan-Meier survival curves for patients who survived the initial IHCA.

SETTING

Intensive care unit, Tongji Hospital, China.

PARTICIPANTS

Adult patients (≥18 years) with IHCA after admission. Pregnant women, patients with 'do not resuscitation' order and patients treated with extracorporeal membrane oxygenation were excluded.

INTERVENTIONS

None.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was the death after IHCA.

RESULTS

Patients (n=561) were divided into two groups: non-sustained return of spontaneous circulation (ROSC) group (n=241) and sustained ROSC group (n=320). Significant differences were found in sex (p=0.006), cardiopulmonary resuscitation (CPR) duration (p<0.001), total duration of CPR (p=0.014), rearrest (p<0.001) and length of stay (p=0.004) between two groups. Multivariate analysis identified that rearrest, duration of CPR and length of stay were independently associated with death. The nomogram including these three factors was well validated using boot calibration plot and exhibited excellent discriminative ability (AUC 0.88, 95% CI 0.83 to 0.93). The tertiles of patients in sustained ROSC group stratified by anticipated probability of death revealed significantly different survival rate (p<0.001).

CONCLUSIONS

Our proposed nomogram based on these three factors is a simple, robust prediction model to accurately predict the death after IHCA.

The value of a machine learning algorithm to predict adverse short-term outcome during resuscitation of patients with in-hospital cardiac arrest: a retrospective study

Background and importance Guidelines recommend that hospital emergency teams locally validate criteria for termination of cardiopulmonary resuscitation in patients with in-hospital cardiac arrest (IHCA). Objective To determine the value of a machine learning algorithm to predict failure to achieve return of spontaneous circulation (ROSC) and unfavourable functional outcome from IHCA using only data readily available at emergency team arrival. Design Retrospective cohort study. Setting and participants Adults who experienced an IHCA were attended to by the emergency team. Outcome measures and analysis Demographic and clinical data typically available at the arrival of the emergency team were extracted from the institutional IHCA database. In addition, outcome data including the Cerebral Performance Category (CPC) score count at hospital discharge were collected. A model selection procedure for random forests with a hyperparameter search was employed to develop two classification algorithms to predict failure to achieve ROSC and unfavourable (CPC 3-5) functional outcomes. Main results Six hundred thirty patients were included, of which 390 failed to achieve ROSC (61.9%). The final classification model to predict failure to achieve ROSC had an area under the receiver operating characteristic curve of 0.9 [95% confidence interval (CI), 0.89-0.9], a balanced accuracy of 0.77 (95% CI, 0.75-0.79), an F1-score of 0.78 (95% CI, 0.76-0.79), a positive predictive value of 0.88 (0.86-0.91), a negative predictive value of 0.61 (0.6-0.63), a sensitivity of 0.69 (0.66-0.72), and a specificity of 0.84 (0.8-0.88). Five hundred fifty-nine subjects experienced an unfavourable outcome (88.7%). The final classification model to predict unfavourable functional outcomes from IHCA at hospital discharge had an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.93), a balanced accuracy of 0.59 (95% CI, 0.57-0.61), an F1-score of 0.94 (95% CI, 0.94-0.95), a positive predictive value of 0.91 (0.9-0.91), a negative predictive value of 0.57 (0.48-0.66), a sensitivity of 0.98 (0.97-0.99), and a specificity of 0.2 (0.16-0.24). Conclusion Using data readily available at emergency team arrival, machine learning algorithms had a high predictive power to forecast failure to achieve ROSC and unfavourable functional outcomes from IHCA while cardiopulmonary resuscitation was still ongoing; however, the positive predictive value of both models was not high enough to allow for early termination of resuscitation efforts.

Predicting neurological outcome in adult patients with cardiac arrest: systematic review and meta-analysis of prediction model performance

This work aims to assess the performance of two post-arrest (out-of-hospital cardiac arrest, OHCA, and cardiac arrest hospital prognosis, CAHP) and one pre-arrest (good outcome following attempted resuscitation, GO-FAR) prediction model for the prognostication of neurological outcome after cardiac arrest in a systematic review and meta-analysis. A systematic search was conducted in Embase, Medline, and Web of Science Core Collection from November 2006 to December 2021, and by forward citation tracking of key score publications. The search identified 1'021 records, of which 25 studies with a total of 124'168 patients were included in the review. A random-effects meta-analysis of C-statistics and overall calibration (total observed vs. expected [O:E] ratio) was conducted. Discriminatory performance was good for the OHCA (summary C-statistic: 0.83 [95% CI 0.81-0.85], 16 cohorts) and CAHP score (summary C-statistic: 0.84 [95% CI 0.82-0.87], 14 cohorts) and acceptable for the GO-FAR score (summary C-statistic: 0.78 [95% CI 0.72-0.84], five cohorts). Overall calibration was good for the OHCA (total O:E ratio: 0.78 [95% CI 0.67-0.92], nine cohorts) and the CAHP score (total O:E ratio: 0.78 [95% CI 0.72-0.84], nine cohorts) with an overestimation of poor outcome. Overall calibration of the GO-FAR score was poor with an underestimation of good outcome (total O:E ratio: 1.62 [95% CI 1.28-2.04], five cohorts). Two post-arrest scores showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the intensive care unit. A pre-arrest score showed acceptable prognostic accuracy and may support code status discussions.

Pre-arrest Prediction of Survival Following In-hospital Cardiac Arrest: A Systematic Review of Diagnostic Test Accuracy Studies

AIM

To evaluate the test accuracy of pre-arrest clinical decision tools for in-hospital cardiac arrest survival outcomes.

METHODS

We searched Medline, Embase, and Cochrane Library from inception through January 2022 for randomized and non-randomized studies. We used the Quality Assessment of Diagnostic Accuracy Studies framework to evaluate risk of bias, and Grading of Recommendations Assessment, Development and Evaluation methodology to evaluate certainty of evidence. We report sensitivity, specificity, positive predictive outcome, and negative predictive outcome for prediction of survival outcomes. PROSPERO CRD42021268005.

RESULTS

We searched 2517 studies and included 23 studies using 13 different scores: 12 studies investigating 8 different scores assessing survival outcomes and 11 studies using 5 different scores to predict neurological outcomes. All were historical cohorts/ case control designs including adults only. Test accuracy for each score varied greatly. Across the 12 studies investigating 8 different scores assessing survival to hospital discharge/ 30-day survival, the negative predictive values (NPVs) for the prediction of survival varied from 55.6% to 100%. The GO-FAR score was evaluated in 7 studies with NPVs for survival with cerebral performance category (CPC) 1 ranging from 95.0% to 99.2%. Two scores assessed survival with CPC ≤2 and these were not externally validated. Across all prediction scores, certainty of evidence was rated as very low.

CONCLUSIONS

We identified very low certainty evidence across 23 studies for 13 different pre-arrest prediction scores to outcome following IHCA. No score was sufficiently reliable to support its use in clinical practice. We identified no evidence for children.

Is frailty associated with long-term survival, neurological function and patient-reported outcomes after in-hospital cardiac arrest? - A Swedish cohort study

BACKGROUND

Frailty is associated with poor 30-days survival after in-hospital cardiac arrests (IHCA). The aim was to assess how pre-arrest frailty was associated with long-term survival, neurological function and patient-reported outcomes in elderly survivors after IHCA.

METHODS

Patients aged ≥65 years with IHCA at Karolinska University Hospital between 2013-2021 were studied. Frailty was assessed by the Clinical Frailty Scale (CFS) based on clinical records and categorised into non-frail (1-4) or frail (5-7). Survival was assessed in days. Neurological function was assessed by the Cerebral Performance Category scale (CPC). A telephone interview was performed six months post-IHCA and included the questionnaires EuroQoL-5 Dimensions-5 Levels and Hospital Anxiety and Depression Scale.

RESULTS

Totally, 232 (28%) out of 817 eligible patients survived to 30-days. Out of 232, 65 (28%) were frail. Long-term survival was better for non-frail than frail patients (6months (92% versus 75%, p-value <0.01), 3 years (74% vs 22%, p-value <0.01)). The vast majority of both non-frail and frail patients had unchanged CPC from admittance to discharge from hospital (87% and 85%, respectively). The 121 non-frail patients reported better health compared to 27 frail patients (EQ-VAS median 70 versus 50 points, p-value <0.01) and less symptoms of depression than frail (16% and 52%, respectively, p-value <0.01).

CONCLUSION

Frail patients suffering IHCA survived with largely unchanged neurological function. Although one in five frail patients survived to three years, frailty was associated with a marked decrease in long-term survival as well as increased symptoms of depression and poorer general health.

Resuscitation highlights in 2021

BACKGROUND

This review is the latest in a series of regular annual reviews undertaken by the editors and aims to highlight some of the key papers published in Resuscitation during 2021.

METHODS

Hand-searching by the editors of all papers published in Resuscitation during 2021. Papers were selected based on then general interest and novelty and were categorised into themes.

RESULTS

98 papers were selected for brief mention.

CONCLUSIONS

Resuscitation science continues to evolve and incorporates all links in the chain of survival.