1
|
Joyce KE, Ashdown K, Delamere JP, Bradley C, Lewis CT, Letchford A, Lucas RAI, Malein W, Thomas O, Bradwell AR, Lucas SJE. Nocturnal pulse oximetry for the detection and prediction of acute mountain sickness: An observational study. Exp Physiol 2024. [PMID: 39277825 DOI: 10.1113/ep091691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 07/31/2024] [Indexed: 09/17/2024]
Abstract
Acute mountain sickness (AMS) is a well-studied illness defined by clinical features (e.g., headache and nausea), as assessed by the Lake Louise score (LLS). Although obvious in its severe form, early stages of AMS are poorly defined and easily confused with common travel-related conditions. Measurement of hypoxaemia, the cause of AMS, should be helpful, yet to date its utility for identifying AMS susceptibility remains unclear. This study quantified altitude-induced hypoxaemia in individuals during an ascent to 4800 m to determine the utility of nocturnal pulse oximetry measurements for prediction of AMS. Eighteen individuals (36 ± 16 years of age) ascended to 4800 m over 12 days. Symptomology of AMS was assessed each morning via LLS criteria, with participants categorized as either AMS-positive (LLS ≥ 3 with headache) or AMS-negative. Overnight peripheral oxygen saturations (ov-S p O 2 ${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ ) were recorded continuously (1 Hz) using portable oximeters. Derivatives of these recordings were compared between AMS-positive and -negative subjects (Mann-Whitney U-test). Exploratory analyses (Pearson's) were conducted to investigate relationships between overnight parameters and AMS severity. Overnight derivatives, including ov-S p O 2 ${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ , heart rate/ov-S p O 2 ${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ , variance, oxygen desaturation index, hypoxic burden and total sleep time at <80%S p O 2 ${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ , all differed significantly between AMS-positive and -negative subjects (all P < 0.01), with cumulative/relative frequency plots highlighting these differences visually. Exploratory analysis revealed that ov-S p O 2 ${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ from 3850 m was correlated with peak LLS at 4800 m (r = 0.58-0.61). The findings highlight the potential for overnight oximetry to predict AMS susceptibility during ascent to high altitude. Further investigation is required to develop, evaluate and optimize predictive models to improve AMS management and prevention.
Collapse
Affiliation(s)
- Kelsey E Joyce
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
| | - Kimberly Ashdown
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Occupational Performance Research Group, University of Chichester, Chichester, UK
| | - John P Delamere
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Medical School, University of Birmingham, Birmingham, UK
| | - Chris Bradley
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Christopher T Lewis
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Department of Anaesthesia, Ysbyty Gwynedd, Bangor, UK
| | - Abigail Letchford
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Greysleydale Healthcare Centre, Swadlincote, UK
| | - Rebekah A I Lucas
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Will Malein
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Department of Anaesthesia, Ninewells Hospital, Dundee, UK
| | - Owen Thomas
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Department of Anaesthesia, Royal Gwent Hospital, NHS Direct Wales, Newport, UK
| | - Arthur R Bradwell
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
- Medical School, University of Birmingham, Birmingham, UK
| | - Samuel J E Lucas
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
- Birmingham Medical Research Expeditionary Society, University of Birmingham, Birmingham, UK
| |
Collapse
|
2
|
Galuzio PP, Cherif A, Tao X, Thwin O, Zhang H, Thijssen S, Kotanko P. Identification of arterial oxygen intermittency in oximetry data. Sci Rep 2022; 12:16023. [PMID: 36163364 PMCID: PMC9511470 DOI: 10.1038/s41598-022-20493-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 09/14/2022] [Indexed: 11/09/2022] Open
Abstract
In patients with kidney failure treated by hemodialysis, intradialytic arterial oxygen saturation (SaO2) time series present intermittent high-frequency high-amplitude oximetry patterns (IHHOP), which correlate with observed sleep-associated breathing disturbances. A new method for identifying such intermittent patterns is proposed. The method is based on the analysis of recurrence in the time series through the quantification of an optimal recurrence threshold ([Formula: see text]). New time series for the value of [Formula: see text] were constructed using a rolling window scheme, which allowed for real-time identification of the occurrence of IHHOPs. The results for the optimal recurrence threshold were confronted with standard metrics used in studies of obstructive sleep apnea, namely the oxygen desaturation index (ODI) and oxygen desaturation density (ODD). A high correlation between [Formula: see text] and the ODD was observed. Using the value of the ODI as a surrogate to the apnea-hypopnea index (AHI), it was shown that the value of [Formula: see text] distinguishes occurrences of sleep apnea with great accuracy. When subjected to binary classifiers, this newly proposed metric has great power for predicting the occurrences of sleep apnea-related events, as can be seen by the larger than 0.90 AUC observed in the ROC curve. Therefore, the optimal threshold [Formula: see text] from recurrence analysis can be used as a metric to quantify the occurrence of abnormal behaviors in the arterial oxygen saturation time series.
Collapse
Affiliation(s)
- Paulo P Galuzio
- Research Division, Renal Research Institute, New York, NY, USA.
| | - Alhaji Cherif
- Research Division, Renal Research Institute, New York, NY, USA.
| | - Xia Tao
- Research Division, Renal Research Institute, New York, NY, USA
| | - Ohnmar Thwin
- Research Division, Renal Research Institute, New York, NY, USA
| | - Hanjie Zhang
- Research Division, Renal Research Institute, New York, NY, USA
| | | | - Peter Kotanko
- Research Division, Renal Research Institute, New York, NY, USA.,Icahn School of Medicine at Mount Sinai Health System, New York, NY, USA
| |
Collapse
|
3
|
Improving the Diagnostic Ability of the Sleep Apnea Screening System Based on Oximetry by Using Physical Activity Data. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00566-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
4
|
Artifact Processing Methods Influence on Intraoperative Hypotension Quantification and Outcome Effect Estimates. Anesthesiology 2020; 132:723-737. [PMID: 32022770 DOI: 10.1097/aln.0000000000003131] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering. METHODS The authors performed a systematic literature search to identify artifact filtering methods. Subsequently, these methods were applied to the data of anesthesia procedures with invasive blood pressure monitoring. Different hypotension measures were calculated (i.e., presence, duration, maximum deviation below threshold, and area under threshold) across different definitions (i.e., thresholds for mean arterial pressure of 50, 60, 65, 70 mmHg). These were then used to estimate the association with postoperative myocardial injury. RESULTS After screening 3,585 papers, the authors included 38 papers that reported artifact filtering methods. The authors applied eight of these methods to the data of 2,988 anesthesia procedures. The occurrence of hypotension (defined with a threshold of 50 mmHg) varied from 24% with a median filter of seven measurements to 55% without an artifact filtering method, and between 76 and 90% with a threshold of 65 mmHg. Standardized odds ratios for presence of hypotension ranged from 1.16 (95% CI, 1.07 to 1.26) to 1.24 (1.14 to 1.34) when hypotension was defined with a threshold of 50 mmHg. Similar variations in standardized odds ratios were found when applying methods to other hypotension measures and definitions. CONCLUSIONS The method of artifact filtering can have substantial effects on estimates of hypotension prevalence. The effect on the association between intraoperative hypotension and postoperative myocardial injury was relatively small. Nevertheless, the authors recommend that researchers carefully consider artifacts handling and report the methodology used.
Collapse
|
5
|
Lin SH, Branson C, Leung J, Park L, Doshi N, Auerbach SH. Oximetry as an Accurate Tool for Identifying Moderate to Severe Sleep Apnea in Patients With Acute Stroke. J Clin Sleep Med 2018; 14:2065-2073. [PMID: 30518446 DOI: 10.5664/jcsm.7538] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 08/16/2018] [Indexed: 12/19/2022]
Abstract
STUDY OBJECTIVES Sleep-disordered breathing (SDB) is highly prevalent in patients with acute stroke. SDB is often underdiagnosed and associated with neurological deterioration and stroke recurrence. Polysomnography or home sleep apnea testing (HSAT) is typically used as the diagnostic modality; however, it may not be feasible to use regularly in patients with acute stroke. We investigated the predictive performance of pulse oximetry, a simpler alternative, to identify SDB. METHODS The records of 254 patients, who were admitted to Boston Medical Center for acute stroke and underwent HSAT, were retrospectively reviewed. Oxygen desaturation index (ODI) from pulse oximetry channel were compared to respiratory event index (REI) obtained from HSAT devices. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ODI were calculated, and different ODI cutoff values to predict SDB were proposed. RESULTS ODI had a strong correlation (r = .902) and agreement with REI. ODI was accurate in predicting SDB at different REI thresholds (REI ≥ 5, REI ≥ 15, and REI ≥ 30 events/h) with the area under the curve (AUC) of .965, .974, and .951, respectively. An ODI ≥ 5 events/h rules in the presence of SDB (specificity 91.7%, PPV 96.3%). An ODI ≥ 15 events/h rules in moderate to severe SDB (specificity 96.4%, PPV 95%) and an ODI < 5 events/h rules out moderate to severe SDB (sensitivity 100%, NPV 100%). CONCLUSIONS Nocturnal pulse oximetry has a high diagnostic accuracy in predicting moderate to severe SDB in patients with acute stroke. Oximetry can be a simple modality to rapidly recognize patients with more severe SDB and facilitate the referral to the confirmation sleep study.
Collapse
Affiliation(s)
- Shih Hao Lin
- Department of Neurology, Boston Medical Center, Boston, Massachusetts
| | - Chantale Branson
- Department of Neurology, Boston Medical Center, Boston, Massachusetts
| | - Jamie Leung
- Boston University School of Medicine, Boston, Massachusetts
| | - Lisa Park
- Boston University School of Medicine, Boston, Massachusetts
| | - Nirmita Doshi
- Boston University School of Medicine, Boston, Massachusetts
| | | |
Collapse
|
6
|
Del Campo F, Crespo A, Cerezo-Hernández A, Gutiérrez-Tobal GC, Hornero R, Álvarez D. Oximetry use in obstructive sleep apnea. Expert Rev Respir Med 2018; 12:665-681. [PMID: 29972344 DOI: 10.1080/17476348.2018.1495563] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
INTRODUCTION Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered: Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection of this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary: Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in populations with significant comorbidities. In the following years, communication technologies and big data analyses will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.
Collapse
Affiliation(s)
- Félix Del Campo
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | - Andrea Crespo
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | | | | | - Roberto Hornero
- b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| | - Daniel Álvarez
- a Pneumology Service , Río Hortega University Hospital , Valladolid , Spain.,b Biomedical Engineering Group , University of Valladolid , Valladolid , Spain
| |
Collapse
|
7
|
Validation of the oxygen desaturation index in the diagnostic workup of obstructive sleep apnea. Sleep Breath 2018; 23:57-63. [DOI: 10.1007/s11325-018-1654-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 03/02/2018] [Accepted: 03/12/2018] [Indexed: 10/17/2022]
|
8
|
Gumb T, Twumasi A, Alimokhtari S, Perez A, Black K, Rapoport DM, Sunderram J, Ayappa I. Comparison of two home sleep testing devices with different strategies for diagnosis of OSA. Sleep Breath 2017; 22:139-147. [PMID: 28823109 DOI: 10.1007/s11325-017-1547-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 07/24/2017] [Accepted: 07/31/2017] [Indexed: 11/25/2022]
Abstract
PURPOSE Home sleep testing devices are being widely used in diagnosis/screening for obstructive sleep apnea (OSA). We examined differences in OSA metrics obtained from two devices with divergent home monitoring strategies, the Apnea Risk Evaluation System (ARES™, multiple signals plus forehead reflectance oximetry) and the Nonin WristOx2™ (single channel finger transmission pulse oximeter), compared to differences from night-night variability of OSA. METHODS One hundred fifty-two male/26 female subjects (BMI = 30.3 ± 5.6 kg/m2, age = 52.5 ± 8.9 years) were recruited without regard to OSA symptoms and simultaneously wore both ARES™ and Nonin WristOx2™ for two nights (n = 351 nights). Automated analysis of the WristOx2 yielded oxygen desaturation index (ODIOx2, ≥4% O2 dips/h), and automated analysis with manual editing of ARES™ yielded AHI4ARES (apneas + hypopneas with ≥4% O2 dips/h) and RDIARES (apneas + hypopneas with ≥4% O2 dips/h or arousal surrogates). Baseline awake oxygen saturation, percent time < 90% O2 saturation (%time < 90%O2Sat), and O2 signal loss were compared between the two methods. RESULTS Correlation between AHI4ARES and ODIOx2 was high (ICC = 0.9, 95% CI = 0.87-0.92, p < 0.001, bias ± SD = 0.7 ± 6.1 events/h). Agreement values for OSA diagnosis (77-85%) between devices were similar to those seen from night-to-night variability of OSA using a single device. Awake baseline O2 saturation was significantly higher in the ARES™ (96.2 ± 1.6%) than WristOx2™ (92.2 ± 2.1%, p < 0.01). There was a significantly lower %time < 90%O2Sat reported by the ARES™ compared to WristOx2 (median (IQR) 0.5 (0.0, 2.6) vs. 2.1 (0.3, 9.7), p < 0.001), and the correlation was low (ICC = 0.2). CONCLUSIONS OSA severity metrics predominantly dependent on change in oxygen saturation and metrics used in diagnosis of OSA (AHI4 and ODI) correlated well across devices tested. However, differences in cumulative oxygen desaturation measures (i.e., %time < 90%O2Sat) between the devices suggest that caution is needed when interpreting this metric particularly in populations likely to have significant hypoxia.
Collapse
Affiliation(s)
- Tyler Gumb
- Division of Pulmonary, Critical Care and Sleep Medicine, New York University, New York, NY, USA
| | - Akosua Twumasi
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY, 10029, USA
| | - Shahnaz Alimokhtari
- Environmental and Occupational Health Sciences Institute, Rutgers Biomedical and Health Sciences, Piscataway, NJ, USA
| | - Alan Perez
- Environmental and Occupational Health Sciences Institute, Rutgers Biomedical and Health Sciences, Piscataway, NJ, USA
| | - Kathleen Black
- Environmental and Occupational Health Sciences Institute, Rutgers Biomedical and Health Sciences, Piscataway, NJ, USA
| | - David M Rapoport
- Division of Pulmonary, Critical Care and Sleep Medicine, New York University, New York, NY, USA.,Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY, 10029, USA
| | - Jag Sunderram
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
| | - Indu Ayappa
- Division of Pulmonary, Critical Care and Sleep Medicine, New York University, New York, NY, USA. .,Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1232, New York, NY, 10029, USA.
| |
Collapse
|