Nocturnal respiratory rate as a predictor of mortality in patients with acute coronary syndrome

Eick, Christian; Groga‐Bada, Patrick; Reinhardt, K.; Duckheim, Martin; Mizera, Lars; Böhm, Katharina; Götz, Nina; Gawaz, Meinrad; Zürn, Christine

doi:10.1136/openhrt-2018-000887

Cited by 5 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because it is simple and easy to observe, the RR is considered one of the indicators for identifying the severity of a patient's condition. Eick et al 40 pointed out in their 2018 study that patients with a higher RR at night have a signi cantly increased mortality rate during their hospital stay and within the following two years. A study from China con rmed that the resting RR at admission combined with the GRACE score has important clinical application value for risk strati cation in ACS patients, and can be used for early warning of the risk of short-term death 41 .…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Mortality Prediction of 90-Day Discharge in Acute Coronary Syndrome Patients

Zhang,

Zhao,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

Background This study aims to develop and validate a novel mortality prediction model to forecast the 90-day mortality risk for patients with ACS (Acute Coronary Syndrome) after discharge. Methods We selected 1359 patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database as our study cohort and collected 32 clinical indicators within the first 24 hours of their admission. By randomly assigning these patients to a training group and a validation group (with a ratio of 0.65:0.35), we used Least Absolute Shrinkage and Selection Operator (LASSO) regression and bidirectional stepwise logistic regression to identify 7 key variables. Based on these variables, we constructed a mortality prediction model. To evaluate the model's accuracy and reliability, we plotted the Receiver Operating Characteristic (ROC) curve, calculated the Area Under the Curve (AUC), sensitivity, and specificity, and performed calibration analysis, including plotting calibration curves, calculating Brier scores, and conducting Hosmer-Lemeshow goodness-of-fit tests. Additionally, through Decision Curve Analysis (DCA) and comparison with current clinical scoring systems, we further assessed the clinical utility of our model. Results Age, SOFA (Sepsis-related Organ Failure Assessment), APS III (Acute Physiology Score III), AG(Anion Gap), RR(Respiratory rate), INR(International normalized ratio), and BUN(Bun urea nitrogen) were identified as independent predictors of 90-day mortality risk. The model demonstrated good diagnostic performance in both the training and validation groups, with AUC values of 0.842 and 0.855, respectively. The Hosmer-Lemeshow test results indicated a good fit for both datasets, with P-values of 0.1626 and 0.4008. The Brier scores were 0.107 for the training set and 0.103 for the validation set, indicating the model's good predictive performance. Compared to existing scoring systems (SOFA, APSIII), DCA showed that our model could provide a higher net benefit in clinical applications. Conclusion We identified seven clinical indicators including age, SOFA, APSIII, AG, RR, INR, and BUN as independent prognostic factors for predicting the 90-day all-cause mortality in patients with ACS after discharge. This model can assist ICU physicians to quickly make preliminary clinical decisions for ACS patients in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Mortality Prediction of 90-Day Discharge in Acute Coronary Syndrome Patients

Zhang,

Zhao,

Guo

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The RHRs that have been linked to an increased vulnerability to illness are not necessarily outside the accepted range of "normal" but rather on the upper end of normal, with most studies reporting a significant increase in disease risk with RHRs above 70 beats/min . Other measures that have been linked to an increased vulnerability to illness include resting respiratory rate (RRR) [31][32][33], resting blood pressure (RBP) [22,34,35], blood pressure variability [34,36], heart rate variability [4,[36][37][38], and heart rate recovery [21,39]. The increased risk of morbidly and mortality associated with these indices can be more than twofold [21,31,37,40].…”

Section: Examining the Mechanism Of The Connectionmentioning

confidence: 99%

“…However, the well-established connection between RHR, autonomic dysregulation, and the development a wide range of mental and physical illnesses strongly suggests that RHR continues to flag the trait of neuronal hyperexcitability whether or not a person is symptomatic. Moreover, in all of the RRR studies, which include RRR in relation to either autonomic function, current illness, or risk of illness, the relationship was not linear but rather dichotomized, with RRRs above 15 breaths/min being independently associated with an elevation in sympathetic drive [49], emotional disorders [44], and all-cause morbidity and mortality [31][32][33]. This dichotomization suggests that there is a gene-related difference between the two groups and, hence, that RRR may be a more reliable marker of the trait of neuronal hyperexcitability and its underlying channelopathies.…”

Section: How To Identify the Vulnerability Traitmentioning

confidence: 99%

FLASH Syndrome: Tapping into the Root of Chronic Illness

Binder¹

2020

AJCEM

View full text Add to dashboard Cite

It has now been established that persons with higher resting heart rates are at increased risk of morbidity and mortality from a plethora of mental and physical illnesses. However, the mechanism underlying the predictive value of this core vital sign remains obscure. This seminal report will integrate clinical, neuropsychological, physiological, and genetic evidence to assert that an inherent hyperexcitability of the neurological system is at the heart of the connection between resting heart rate and disease. Hypothetically, neuronal hyperexcitability can cause multiple circuits in the brain to overfire, including cognitive circuits, limbic circuits, and autonomic circuits, thereby dysregulating the associated systems of the body and allowing the aberration to be detected through standard vital signs and related measures of autonomic activity. Because the cognitive-emotional system is exquisitely sensitive to neuronal excitation, the aberration could also manifest as psychiatric symptomatology, thus suggesting that psychiatric symptoms may be the first subjective markers of the abnormality. Based on the well-recognized link between mental illness, autonomic dysregulation, and systemic disease together with mounting evidence that the related illnesses are associated with gene variants whose protein products fail to adequately regulate the firing of neurons, the vulnerability trait could aptly be called Familial Limbic Autonomic System Hyperexcitability or "FLASH." Because the trait appears to be so common, its effects so pervasive, and its expression so modifiable, its identification is of critical importance to every medical specialty. FLASH could give clinicians the first comprehensive biological target through which to treat and prevent a plethora of mental, emotional, and physical illnesses.

show abstract

“…In particular, RR during sleep can be used to estimate sleep stage by observing the variations in the RR throughout a single sleep session [ 1 , 2 ]. Moreover, RR assessment has proven valuable in detecting clinical conditions, including COVID-19 infection [ 3 ] and exacerbation of chronic obstructive pulmonary disease [ 4 ], and serving as an indicator of acute coronary syndrome [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring

Jung,

Kim,

Choi

et al. 2023

Sensors

View full text Add to dashboard Cite

Wrist-based respiratory rate (RR) measurement during sleep faces accuracy limitations. This study aimed to assess the accuracy of the RR estimation function during sleep based on the severity of obstructive sleep apnea (OSA) using the Samsung Galaxy Watch (GW) series. These watches are equipped with accelerometers and photoplethysmography sensors for RR estimation. A total of 195 participants visiting our sleep clinic underwent overnight polysomnography while wearing the GW, and the RR estimated by the GW was compared with the reference RR obtained from the nasal thermocouple. For all participants, the root mean squared error (RMSE) of the average overnight RR and continuous RR measurements were 1.13 bpm and 1.62 bpm, respectively, showing a small bias of 0.39 bpm and 0.37 bpm, respectively. The Bland–Altman plots indicated good agreement in the RR measurements for the normal, mild, and moderate OSA groups. In participants with normal-to-moderate OSA, both average overnight RR and continuous RR measurements achieved accuracy rates exceeding 90%. However, for patients with severe OSA, these accuracy rates decreased to 79.45% and 75.8%, respectively. The study demonstrates the GW’s ability to accurately estimate RR during sleep, even though accuracy may be compromised in patients with severe OSA.

show abstract

Nocturnal respiratory rate as a predictor of mortality in patients with acute coronary syndrome

Cited by 5 publications

References 25 publications

Machine Learning-Based Mortality Prediction of 90-Day Discharge in Acute Coronary Syndrome Patients

Machine Learning-Based Mortality Prediction of 90-Day Discharge in Acute Coronary Syndrome Patients

FLASH Syndrome: Tapping into the Root of Chronic Illness

Validating a Consumer Smartwatch for Nocturnal Respiratory Rate Measurements in Sleep Monitoring

Contact Info

Product

Resources

About