Timing errors and temporal uncertainty in clinical databases-A narrative review

Electronic health record data is unable to effectively characterize measurement error from pulse oximetry: a simulation study

Large data sets from electronic health records (EHR) have been used in journal articles to demonstrate race-based imprecision in pulse oximetry (SpO2) measurements. These articles do not appear to recognize the impact of the variability of the SpO2 values with respect to time ("deviation time"). This manuscript seeks to demonstrate that due to this variability, EHR data should not be used to quantify SpO2 error. Using the MIMIC-IV Waveform dataset, SpO2 values are sampled from 198 patients admitted to an intensive care unit and used as reference samples. The error derived from the EHR data is simulated using a set of deviation times. The laboratory oxygen saturation measurements are also simulated such that the performance of three simulated pulse oximeter devices will produce an average root mean squared (ARMS) error of 2%. An analysis is then undertaken to reproduce a medical device submission to a regulatory body by quantifying the mean error, the standard deviation of the error, and the ARMS error. Bland-Altman plots were also generated with their Limits of Agreements. Each analysis was repeated to evaluate whether the measurement errors were affected by increasing the deviation time. All error values increased linearly with respect to the logarithm of the time deviation. At 10 min, the ARMS error increased from a baseline of 2% to over 4%. EHR data cannot be reliably used to quantify SpO2 error. Caution should be used in interpreting prior manuscripts that rely on EHR data.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Appropriateness of antibiotic prescribing for patients with sepsis in rural hospital emergency departments

DESIGN/PARTICIPANTS

This was a multicentre retrospective cohort study of adult patients (≥18 years) presenting with a process associated International Classification of Diseases code (ICD-AM-10) pertaining to sepsis between January 2017 and July 2020 to rural Emergency Departments.

MAIN OUTCOME MEASURES

Our primary outcome was antibiotic appropriateness as defined by the Australian Therapeutic Guidelines (for antibiotic selection relative to infecting source) and the National Antimicrobial Prescribing Survey tool. Our secondary outcome was in-hospital mortality.

METHODS

Relevant clinical and non-clinical, physiological and laboratory data were collected retrospectively. Multivariate logistic regression was used to estimate the odds of both inappropriate antibiotic prescribing and in-hospital mortality based on clinical and non-clinical factors.

RESULTS

A total of 378 patients were included who primarily presented with sepsis of unknown origin (36.8%), a genitourinary (22.22%) or respiratory (18.78%) source. Antibiotics were appropriately prescribed in 59% of patients. A positive Quick Sequential Organ Failure Assessment score (qSOFA) (odds ratio [OR] = 0.49; 95% confidence interval [CI], 0.29-0.83), a respiratory infection source (OR = 0.5; 95% CI, 0.29-0.86) and documented allergy (OR = 0.42; 95% CI, 0.25-0.72) were associated with a lower risk of appropriate prescribing in multivariate analysis. Forty-one percent of patients received antibiotics within 1 h of presentation. Inappropriate antibiotic prescribing was not associated with in-hospital mortality.

CONCLUSION

The rates of appropriate antibiotic prescribing in rural Emergency Departments for patients presenting with sepsis is low, but comparable to other referral metropolitan centres.

Cumulative dose of epinephrine and mode of death after non-shockable out-of-hospital cardiac arrest: a registry-based study

BACKGROUND

Epinephrine increases the chances of return of spontaneous circulation (ROSC) in out-of-hospital cardiac arrest (OHCA), especially when the initial rhythm is non-shockable. However, this drug could also worsen the post-resuscitation syndrome (PRS). We assessed the association between epinephrine use during cardiopulmonary resuscitation (CPR) and subsequent intensive care unit (ICU) mortality in patients with ROSC after non-shockable OHCA.

METHODS

We used data prospectively collected in the Sudden Death Expertise Center (SDEC) registry (capturing OHCA data located in the Greater Paris area, France) between May 2011 and December 2021. All adults with ROSC after medical, cardiac and non-cardiac causes, non-shockable OHCA admitted to an ICU were included. The mode of death in the ICU was categorized as cardiocirculatory, neurological, or other.

RESULTS

Of the 2,792 patients analyzed, there were 242 (8.7%) survivors at hospital discharge, 1,004 (35.9%) deaths from cardiocirculatory causes, 1,233 (44.2%) deaths from neurological causes, and 313 (11.2%) deaths from other etiologies. The cardiocirculatory death group received more epinephrine (4.6 ± 3.8 mg versus 1.7 ± 2.8 mg, 3.2 ± 2.6 mg, and 3.5 ± 3.6 mg for survivors, neurological deaths, and other deaths, respectively; p < 0.001). The proportion of cardiocirculatory death increased linearly (R2 = 0.92, p < 0.001) with cumulative epinephrine doses during CPR (17.7% in subjects who did not receive epinephrine and 62.5% in those who received > 10 mg). In multivariable analysis, a cumulative dose of epinephrine was strongly associated with cardiocirculatory death (adjusted odds ratio of 3.45, 95% CI [2.01-5.92] for 1 mg of epinephrine; 12.28, 95% CI [7.52-20.06] for 2-5 mg; and 23.71, 95% CI [11.02-50.97] for > 5 mg; reference 0 mg; population reference: alive at hospital discharge), even after adjustment on duration of resuscitation. The other modes of death (neurological and other causes) were also associated with epinephrine use, but to a lesser extent.

CONCLUSIONS

In non-shockable OHCA with ROSC, the dose of epinephrine used during CPR is strongly associated with early cardiocirculatory death. Further clinical studies aimed at limiting the dose of epinephrine during CPR seem warranted. Moreover, strategies for the prevention and management of PRS should take this dose of epinephrine into consideration for future trials.

The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.