Inaccuracy of patient care reports for identification of critical resuscitation events during out-of-hospital cardiac arrest

A retrospective, multi-agency 'target trial emulation' for the comparison of post-resuscitation epinephrine to norepinephrine

INTRODUCTION

Epinephrine and norepinephrine are the two most commonly used prehospital vasopressors in the United States. Prior studies have suggested that use of a post-ROSC epinephrine infusion may be associated with increased rearrest and mortality in comparison to use of norepinephrine. We used target trial emulation methodology to compare the rates of rearrest and mortality between the groups of OHCA patients receiving these vasopressors in the prehospital setting.

METHODS

Adult (18-80 years of age) non-traumatic OHCA patients in the 2018-2022 ESO Data Collaborative datasets with a documented post-ROSC norepinephrine or epinephrine infusion were included in this study. Logistic regression modeling was used to evaluate the association between vasopressor agent and outcome using two sets of covariables. The first set of covariables included standard Utstein factors, the dispatch to ROSC interval, the ROSC to vasopressor interval, and the follow-up interval. The second set added prehospital systolic blood pressure and SpO2 values. Kaplan-Meier time-to-event analysis was also conducted and the vasopressor groups were compared using a multivariable Cox regression model.

RESULTS

Overall, 1,893 patients treated by 309 EMS agencies were eligible for analysis. 1,010 (53.4%) received an epinephrine infusion and 883 (46.7%) received a norepinephrine infusion as their initial vasopressor. Adjusted analyses did not discover an association between vasopressor agent and rearrest (aOR: 0.93 [0.72, 1.21]) or mortality (aOR: 1.00 [0.59, 1.69]).

CONCLUSIONS

In this multi-agency target trial emulation, the use of a post-resuscitation epinephrine infusion was not associated with increased odds of rearrest in comparison to the use of a norepinephrine infusion.

Database quality assessment in research in paramedicine: a scoping review

BACKGROUND

Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine.

METHODS

This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form.

RESULTS

Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness.

CONCLUSIONS

There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.

Comparison of four clinical risk scores in comatose patients after out-of-hospital cardiac arrest

BACKGROUND AND AIMS

Several different scoring systems for early risk stratification after out-of-hospital cardiac arrest have been developed, but few have been validated in large datasets. The aim of the present study was to compare the well-validated Out-of-hospital Cardiac Arrest (OHCA) and Cardiac Arrest Hospital Prognosis (CAHP)-scores to the less complex MIRACLE2- and Target Temperature Management (TTM)-scores.

METHODS

This was a post-hoc analysis of the Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. Missing data were handled by multiple imputation. The primary outcome was discriminatory performance assessed as the area under the receiver operating characteristics-curve (AUROC), with the outcome of interest being poor functional outcome or death (modified Rankin Scale 4-6) at 6 months after OHCA.

RESULTS

Data on functional outcome at 6 months were available for 1829 cases, which constituted the study population. The pooled AUROC for the MIRACLE2-score was 0.810 (95% CI 0.790-0.828), 0.835 (95% CI 0.816-0.852) for the TTM-score, 0.820 (95% CI 0.800-0.839) for the CAHP-score and 0.770 (95% CI 0.748-0.791) for the OHCA-score. At the cut-offs needed to achieve specificities >95%, sensitivities were <40% for all four scoring systems.

CONCLUSIONS

The TTM-, MIRACLE2- and CAHP-scores are all capable of providing objective risk estimates accurate enough to be used as part of a holistic patient assessment after OHCA of a suspected cardiac origin. Due to its simplicity, the MIRACLE2-score could be a practical solution for both clinical application and risk stratification within trials.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Data quality assessment in emergency medical services: an objective approach

BACKGROUND

In emergency medical services, high quality data are of great importance for patient care. Due to the unique nature of this type of services, the purpose of this study was to assess data quality in emergency medical services using an objective approach.

METHODS

This was a retrospective quantitative study conducted in 2019. The research sample included the emergency medical records of patients who referred to three emergency departments by the pre-hospital emergency care services (n = 384). Initially a checklist was designed based on the data elements of the triage form, pre-hospital emergency care form, and emergency medical records. Then, data completeness, accuracy and timeliness were assessed.

RESULTS

Data completeness in the triage form, pre-hospital emergency care form, and emergency medical records was 52.3%, 70% and 57.3%, respectively. Regarding data accuracy, most of the data elements were consistent. Measuring data timeliness showed that in some cases, paper-based ordering and computer-based data entry was not sequential.

CONCLUSION

Data quality in emergency medical services was not satisfactory and there were some weaknesses in the documentation processes. The results of this study can inform the clinical and administrative staff to pay more attentions to these weaknesses and plan for data quality improvement.

Evaluation of comprehensiveness and reliability of electronic health records concerning resuscitation efforts within academic intensive care units: a retrospective chart analysis

Background

According to the literature, the validity and reliability of medical documentation concerning episodes of cardiopulmonary resuscitation (CPR) is suboptimal. However, little is known about documentation quality of CPR efforts during intensive care unit (ICU) stays in electronic patient data management systems (PDMS). This study analyses the reliability of CPR-related medical documentation within the ICU PDMS.

Methods

In a retrospective chart analysis, PDMS records of three ICUs of a single university hospital were searched over 5 y for CPR check marks. Respective datasets were analyzed concerning data completeness and data consistency by comparing the content of three documentation forms (physicians’ log, nurses’ log, and CPR incident form), as well as physiological and therapeutic information of individual cases, for missing data and plausibility of CPR starting time and duration. To compare data reliability and completeness, a quantitative measure, the Consentaneity Index (CI), is proposed.

Results

One hundred sixty-five datasets were included into the study. In 9% (n = 15) of cases, there was neither information on the time points of CPR initiation nor on CPR duration available in any data source. Data on CPR starting time and duration were available from at least two data sources in individual cases in 54% (n = 90) and 45% (n = 74), respectively. In these cases, the specifications of CPR starting time did differ by a median ± interquartile range of 10.0 ± 18.5 min, CPR duration by 5.0 ± 17.3 min. The CI as a marker of data reliability revealed a low consistency of CPR documentation in most cases, with more favorable results, if the time interval between the CPR episode and the time of documentation was short.

Conclusions

This study reveals relevant proportions of missing and inconsistent data in electronic CPR documentation in the ICU setting. The CI is suggested as a tool for documentation quality analysis and monitoring of improvements.

Impact of video-recording on patient outcome and data collection in out-of-hospital cardiac arrests

BACKGROUND

Most research on out-of-hospital resuscitation relies on data collection from medical records. However, the data in medical records are often inaccurate.

OBJECTIVE

To compare the data registration of the medical record with the data from the video recorded resuscitation and study the impact of video recording during resuscitation on the outcome.

METHODS

Out-of-hospital cardiopulmonary resuscitation (CPR) was video recorded using a body-mounted camera. Video recordings were independently reviewed and compared with the data of the medical record. The presence of bystander CPR and witnessed arrest, the initial rhythm, total number of defibrillations, adrenaline dosage and the total duration of CPR were studied. Using the medical records, CPR outcomes were compared for the periods prior to, during and after video recording.

RESULTS

In total, 129 resuscitations were analysed. Of the six parameters, only the number of defibrillations was not significantly different in the medical record compared to the video recordings. The total duration of CPR (69.0%) and the total dose of adrenaline administered (63.6%) were the most incorrectly recorded, followed by the number of defibrillations (34.0%), witnessed arrest (31.0%), bystander CPR (24.0%) and initial rhythm (7%). No statistically significant difference was found comparing the outcomes (ROSC, 24 h and 1 month survival) of the periods before, during and after video recording.

CONCLUSION

We detected a high number of discrepancies between the medical record and the data from the video recorded resuscitation. No significant effect of video-recording on patient outcome was found.

Usefulness of the NULL-PLEASE Score to Predict Survival in Out-of-Hospital Cardiac Arrest

PURPOSE

Out-of-hospital cardiac arrest (OHCA) carries a very high mortality rate even after successful cardiopulmonary resuscitation. Currently, information given to relatives about prognosis following resuscitation is often emotive and subjective, and varies with clinician experience. We aimed to validate the NULL-PLEASE score to predict survival following OHCA.

METHODS

A multicenter cohort study was conducted, with retrospective and prospective validation in consecutive unselected patients presenting with OHCA. The NULL-PLEASE score was calculated by attributing points to the following variables: Nonshockable initial rhythm, Unwitnessed arrest, Long low-flow period, Long no-flow period, pH <7.2, Lactate >7.0 mmol/L, End-stage renal failure, Age ≥85 years, Still resuscitation, and Extracardiac cause. The primary outcome was in-hospital death.

RESULTS

We assessed 700 patients admitted with OHCA, of whom 47% survived to discharge. In 300 patients we performed a retrospective validation, followed by prospective validation in 400 patients. The NULL-PLEASE score was lower in patients who survived compared with those who died (0 [interquartile range 0-1] vs 4 [interquartile range 2-4], P < .0005) and strongly predictive of in-hospital death (C-statistic 0.874; 95% confidence interval, 0.848-0.899). Patients with a score ≥3 had a 24-fold increased risk of death (odds ratio 23.6; 95% confidence interval, 14.840-37.5; P < .0005) compared with those with lower scores. A score ≥3 has a 91% positive predictive value for in-hospital death, while a score <3 predicts a 71% chance of survival.

CONCLUSION

The easy-to-use NULL-PLEASE score predicts in-hospital mortality with high specificity and can help clinicians explain the prognosis to relatives in an easy-to-understand, objective fashion, to realistically prepare them for the future.

A Review of Data Quality Assessment in Emergency Medical Services

INTRODUCTION

Data quality is an important issue in emergency medicine. The unique characteristics of emergency care services, such as high turn-over and the speed of work may increase the possibility of making errors in the related settings. Therefore, regular data quality assessment is necessary to avoid the consequences of low quality data. This study aimed to identify the main dimensions of data quality which had been assessed, the assessment approaches, and generally, the status of data quality in the emergency medical services.

METHODS

The review was conducted in 2016. Related articles were identified by searching databases, including Scopus, Science Direct, PubMed and Web of Science. All of the review and research papers related to data quality assessment in the emergency care services and published between 2000 and 2015 (n=34) were included in the study.

RESULTS

The findings showed that the five dimensions of data quality; namely, data completeness, accuracy, consistency, accessibility, and timeliness had been investigated in the field of emergency medical services. Regarding the assessment methods, quantitative research methods were used more than the qualitative or the mixed methods. Overall, the results of these studies showed that data completeness and data accuracy requires more attention to be improved.

CONCLUSION

In the future studies, choosing a clear and a consistent definition of data quality is required. Moreover, the use of qualitative research methods or the mixed methods is suggested, as data users' perspectives can provide a broader picture of the reasons for poor quality data.