Relationship between apnoea duration and changes in physiology in preterm neonates: a systematic review and meta-analysis

Relationship between apnoea duration and changes in physiology in preterm neonates: a systematic review and meta-analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Relationship between apnoea duration and changes in physiology in preterm neonates: a systematic review and meta-analysis Yiru Chen , Coen S. Zandvoort , View ORCID Profile Luke Baxter , Odunayo A. T. Fatunla , Vithushanan Ketheeswaranathan , Ravi Poorun , Zara Small , Fatima Usman , Matthew Henry , Luc Berthouze , Mauricio Villarroel , Caroline Hartley doi: https://doi.org/10.1101/2025.04.04.25325244 Yiru Chen 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Coen S. Zandvoort 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luke Baxter 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luke Baxter Odunayo A. T. Fatunla 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vithushanan Ketheeswaranathan 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ravi Poorun 2 University of Exeter Medical School, University of Exeter , Exeter, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zara Small 3 Medical Sciences Division, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fatima Usman 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew Henry 4 Bodleian Health Care Libraries, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luc Berthouze 5 Department of Informatics, University of Sussex , Brighton, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mauricio Villarroel 6 Department of Engineering Science, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caroline Hartley 1 Department of Paediatrics, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: caroline.hartley{at}paediatrics.ox.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Apnoea is a common respiratory complication in preterm neonates, leading to substantial changes in physiology. We conducted this systematic review and meta-analysis to examine the relationship between apnoea duration and changes in physiology in preterm neonates, and to identify factors that modulate this relationship. Methods We searched Medline, EMBASE, PsycINFO, and Cochrane Central Register of Controlled Trials databases and included primary empirical studies examining the relationship between apnoea or respiratory pause duration and at least one outcome (heart rate, blood oxygen saturation, cerebral oxygenation, cerebral blood volume) in hospitalised neonates with postmenstrual age (PMA) <37 weeks. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal checklist. Results were synthesised narratively and quantitative data was pooled for meta-analysis. Results Forty-two papers were included, involving a total of 1,483 neonates with 2,399 study sessions. The decrease in heart rate, oxygen saturation, and cerebral oxygenation were significantly correlated with apnoea duration. PMA significantly modulated the relationship, with younger neonates more likely to exhibit oxygen desaturation from short apnoeas. Conclusions These findings indicate that shorter apnoea alarm thresholds should be considered for younger infants. Impact statement: What is the key message of your article? Systematic review and meta-analysis of the relationship between change in physiology and apnoea duration in preterm infants. What does it add to the existing literature? Through meta-analysis, we demonstrate that postmenstrual age plays a significant modulating role in the relationship between apnoea duration and change in oxygen saturation, with younger infants more likely to have significant desaturations. What is the impact? We propose that age-stratified apnoea alarm limits are considered to prevent physiological instability in newborns. 1. Introduction Globally, approximately 10% of neonates are born preterm (before 37 weeks of gestation) 1 . Apnoea, the cessation of breathing, is a common respiratory complication for these neonates, affecting more than 50%, especially those born extremely prematurely (before 28 weeks of gestation) 2 . Apnoea can occur multiple times per day and can lead to significant physiological changes, including alterations in heart rate, blood oxygen saturation, cerebral oxygenation, and cerebral blood volume 3 – 6 . While most episodes resolve spontaneously or with minimal intervention, recurrent or prolonged episodes may have clinical significance and require appropriate monitoring and management. Additionally, apnoea has been linked to long-term complications, such as an increased incidence of retinopathy of prematurity (ROP) 7 and cognitive deficits later in life 8 , 9 . These may be related to the extent of apnoea-induced hypoxia or brain activity changes 10 . There is wide variability within and between neonates in how they respond to an apnoea of a given duration, with short pauses in breathing sometimes leading to large changes in physiology and other times much longer pauses in breathing not leading to significant changes in physiology 4 . Factors such as the age of the neonate may alter physiological responses to an apnoea, with older babies able to tolerate longer periods of apnoea without changes in physiology. It also seems plausible that co-morbidities and medication have an impact. For example, caffeine reduces the incidence of apnoea and intermittent hypoxaemia 11 – 13 . A better understanding of the factors which modulate the impact of apnoea on physiology is crucial to identify neonates at the highest risk of adverse physiological changes. Ultimately, this could lead to the development of predictive models that can guide treatment or facilitate closer monitoring for high-risk neonates. We conducted this systematic review and meta-analysis to examine the current knowledge regarding the relationship between apnoea duration and physiological changes (heart rate, oxygen saturation, cerebral oxygenation, and cerebral blood flow) in preterm neonates, and to investigate factors which modulate these relationships. Specifically, we aimed to investigate the following questions: What are the relationships between the duration of pauses in breathing/apnoea and changes in heart rate, blood oxygen saturation, cerebral oxygenation, and cerebral blood volume in hospitalised premature neonates? How do these relationships vary across neonates with different postmenstrual age (PMA), pathology (specifically sepsis and necrotising enterocolitis [NEC]), medication (specifically methylxanthines and opioids), and any other potential modulating factors identified in the included papers? How are these relationships modulated by the frequency/clustering of pauses in breathing? 2. Methods This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement 14 . The protocol for this systematic review was registered in PROSPERO (CRD42024534164). 2.1 Eligibility criteria All primary empirical and peer-reviewed studies reporting a relationship between apnoea or respiratory pause duration and at least one of the outcomes (change in heart rate, blood oxygen saturation, cerebral oxygenation and cerebral blood volume) in hospitalised human neonates with PMA < 37 weeks were included. We excluded papers with the wrong population (non-human species; pre-natal humans; full-term neonates (PMA = 37 weeks) and older (e.g., children, and adult subjects); neonates with neurological abnormalities (e.g. seizures) or congenital abnormalities; and non-hospitalised neonates. We also excluded papers with the incorrect study characteristics (secondary literature (e.g. reviews, book chapters); non-empirical literature (e.g. opinions, commentaries, perspectives); non-peer-reviewed grey literature (e.g. conference abstracts, meeting reports, theses); and study protocols). There were no restrictions based on publication language or date. 2.2 Search strategy A combination of subject headings terms and controlled keywords were used, and searches were conducted initially on 23 November 2023 and updated on 10 December 2024 in the following databases: MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid) and Cochrane Central Register of Controlled Trials (Cochrane Library, Wiley). Detailed searching strategies are included in Supplementary Methods. Additionally, backward citation tracking was carried out on the final set of papers as an additional information source, and this was done through the Citationchaser package 15 . 2.3 Selection process A systematic review management tool, Covidence (Melbourne, Australia), was used to manage records and data throughout the review. The references were imported onto the platform, and automatic de-duplication was performed. In total, seven reviewers were involved in the selection process. The selection followed a two-stage process: 1. A title and abstract screening against the inclusion criteria to identify potentially relevant papers. 2. A full-text screening of all the papers identified as possibly relevant for inclusion from the initial screening above. At the title and abstract screening stage, a pilot block of randomly selected 20 articles was screened by all reviewers to ensure consistency of reviewers across the screening process. Papers were then divided into blocks, with two independent reviewers assigned to each block. Papers whose abstract did not describe any form of relationship between apnoea and changes in vital signs were excluded. At the full-text screening stage, the same two reviewers assessed all papers, except those written in non-English languages. Papers written in Dutch, German, Italian, or French were reviewed by fluent speakers of the respective language, with an additional reviewer using Google Translate to validate the results. One paper in Bulgarian and one in Danish were screened using Google Translate only and excluded at the title and abstract screening stage. At both stages, screening was carried out in duplicate and independently by the two reviewers, and inconsistencies were settled by discussion between both reviewers and if necessary, an arbitrator. For consistency, one reviewer (YC) acted across all papers. The same arbitrator (LBa) also acted across all papers. 2.4 Data collection process and data items Data extraction was carried out by a single reviewer (YC). When relevant information was provided only in figure format, the PlotDigitizer software was used to extract values from the figures. This process was conducted twice for each figure to ensure consistency in the results. The following variables were extracted: author’s names; year of publication; country and region of publication (according to World Health Organisation categorisation 16 ); hospital(s) where data was collected, study period; study design; sample size; neonate’s age (gestational age and postmenstrual age), including median and range where available; type of respiratory support received by the neonates (if any); the pathological conditions of the neonates (specifically sepsis and NEC); type of medication used (if any); description of apnoea/pause in breathing (i.e., duration, method of measurement, any resuscitation provided, information on the frequency of pause/clustering of pauses); description of the changes in outcome variables (i.e., absolute change, baseline and minimum/maximum values if provided, method of measurement); duration of the recordings and time of day; signal quality measures (if given). 2.5 Study risk of bias assessment (quality assessment) Assessment of the risk of bias in the included studies was carried out by using the checklist for systematic reviews and research syntheses provided by the Joanna Briggs Institute Critical Appraisal tools. Two reviewers (YC and CZ) evaluated the risk of bias independently. Any disagreement between the two reviewers was settled through discussion. To derive an overall risk of bias rating 17 , 18 , we focused exclusively on items within the JBI checklists related to internal validity. Each item in the relevant JBI checklists was classified as either “critical” (pertaining to internal validity) or “non-critical” (pertaining to other constructs). Critical items were mapped to established domains of bias, including selection bias, confounding, measurement bias, temporal bias, and attrition bias. Studies were then rated based on their responses to critical items, following a predefined rule set: low risk of bias was defined as no “no” responses on critical items; moderate risk of bias was defined as one “no” response on a critical item; high risk of bias was defined as two or more “no” responses on critical items. Responses of “unclear” or “not applicable” were not treated as indicators of bias, ensuring a conservative and transparent assessment approach. 2.6 Data synthesis The outcome of the database searches and study selection process was presented in a PRISMA flowchart. The data synthesis was performed using a comprehensive narrative approach to ensure a rigorous and informative analysis. Narrative and qualitative synthesis involved the tabulation of results where feasible. Quantitative data from the selected studies regarding the relationship between apnoea duration and physiological changes in preterm neonates were extracted and pooled for meta-analysis where available. The dependent variable were the mean percentage changes in vital signs. If a study provided the values for percentage change in vital signs directly, these were used for the meta-analysis. When values at the beginning and end of apnoea were provided, the difference was calculated and divided by the starting value to determine the percentage change. The independent variable was the apnoea durations. If a study provided the mean apnoea durations for each pooled point, these were used directly. When only a range of apnoea durations was provided, the midpoint of the range was used as the X-value (e.g., if the range of apnoea duration was 10 – 20 seconds, the X-value would be 15 seconds for that point). 19 , 20 If multiple studies presented the same results using the same dataset, then we only included the results once in the meta-analysis. Pooled data were analysed using Pearson correlation coefficients, linear regression models, and linear mixed-effects models. For the linear regression models, apnoea duration was the predictor, and changes in vital signs were the response. The square root of the number of apnoea events associated with each data point was used as the weight in the model. The coefficient of determination (R 2 ) was used to assess how much of the variation in vital signs could be explained by apnoea duration. The formula to calculate R 2 was: where SS RES is the sum of squares of residuals, and SS tot is the total sum of squares. For the linear mixed-effects models, the study ID number was used as a random effect, and apnoea duration as a fixed effect. The impact of apnoea type, PMA, and the use of theophylline (a medication that is commonly used for apnoea treatment) on the relationship between apnoea duration and physiological changes was analysed, where applicable, by adding interaction terms between these factors and apnoea duration as fixed effects in the model. For a post hoc analysis of how PMA affects the relationship between changes in oxygen saturation (SpO2) and apnoea duration, we used a linear regression model with the formula: Using this model, we predicted the apnoea duration at which oxygen saturation is likely to drop by more than 10% for neonates of different PMAs. The 10% threshold is commonly used for defining neonatal desaturation 21 . The correlation coefficients from different papers reporting the same outcome were standardised and converted into a common effect size metric, Fisher’s Z score, which was combined to provide overall correlation factors. The regression lines from the individual studies were plotted alongside the line obtained from the meta-analysis for visual comparison of the variation across studies. 3. Results A total of 4,362 studies were identified, after the removal of 8,567 duplicates ( Figure 1 ). Following the title and abstract screening, 4,190 studies were excluded, and a further 129 studies were excluded after the full-text review. One study 22 was excluded due to unavailability of the full text after database searches and inter-library requests. Overall, 42 studies were included in this review, involving a total of 1,483 neonates with 2,399 recording sessions (i.e. a period during which a patient’s vital signs are recorded). The median number of neonates in each study was 24 (range: 1 – 335, IQR: 14 – 31.75), and the median number of recording sessions was 31 (range: 1 – 386, IQR: 18 – 78). Twenty-three of the included studies (54.8%) were cross-sectional, 16 (38.1%) were cohort studies, 2 (4.8%) were case-control studies, and 1 (2.4%) was a case report. The year of publication ranged from 1969 to 2022, with a median of 1992. Most of the studies were from Europe (n = 21, 50%) and the Americas (n = 17, 40.5%). The remainder of studies were from the Western Pacific (n = 4, 9.5%). No studies were published from the South-East Asian Region, Eastern Mediterranean or Africa (regions classified using the World Health Organisation categorisation 23 ). Download figure Open in new tab Figure 1: Prisma flow chart showing the study selection process. Thirty-one articles (73.8%) described the relationship between apnoea duration and change in heart rate, 24 articles (57.1%) described the relationship between apnoea duration and change in oxygen saturation, 3 studies (7.1%) described the relationship between apnoea duration and change in cerebral oxygenation, and only one study (2.4%) described the relationship between apnoea duration and change in cerebral blood volume. The definition of apnoea and pauses in breathing investigated in the different studies varied; some studies included pauses in breathing as short as 3 seconds, while other studies only included apnoeas with a duration of 20 seconds or more. The techniques used to measure apnoea also varied, including polysomnography, acoustic monitoring, inductive plethysmography, thermistor, and pneumotachograph ( Tables 1 - 4 ). View this table: View inline View popup Table 1: Summary of included studies reporting the relationship between apnoea duration and change in heart rate. ECG: Electrocardiogram. SD: standard deviation. 3.1 Relationship between apnoea duration and change in heart rate A total of 31 articles investigated the change in heart rate compared with apnoea duration ( Table 1 ), involving of 1008 neonates and 1734 recording sessions in total. Most studies (n = 26, 83.9%) measured heart rate using the electrocardiogram (ECG), while some used different monitors ( Table 1 ). The relationship between heart rate changes and apnoea duration was examined using various methods, including correlation coefficients, linear regression models, and scatter plot diagrams. Most studies demonstrated that a longer apnoea duration is correlated with a greater reduction in heart rate. Six studies 4 , 24 – 28 reported correlation coefficients, with all identifying a significant correlation between apnoea duration and metrics relating to decreased heart rate. Five studies 29 – 33 presented the mean change in heart rate during apnoea for different apnoea duration subgroups and were included in the meta-analysis. The average of the mean PMA of the neonates across the five studies was 32.84 ± 1.56 (mean ± SD). The pooled data across all five studies did not show a significant relationship between apnoea duration and heart rate change (Supplementary Figure 1, n = 5 studies; slope = −0.13, R 2 = −0.02; r = −0.28, p = 0.10). However, data from Finer et al. (1992) 30 showed an opposite trend to the other four studies, demonstrating a smaller change in heart rate for longer apnoeas. This might be due to the study’s unique definition of apnoea – the authors only recorded apnoeas associated with bradycardia or hypoxemia, excluding shorter pauses with less significant decrease in heart rate. Removing this study from further meta-analysis, the pooled data from the other four studies showed a negative relationship between apnoea duration and percentage change in heart rate during apnoea ( Figure 2A , n = 4 studies; slope = −1.54, R 2 = 0.36; r = −0.60, p = 0.0039). Download figure Open in new tab Figure 2: Meta-analysis of change in physiology compared with apnoea duration. (A) The percentage change in heart rate during apnoea compared with apnoea duration. (B) The percentage of apnoea events accompanied by bradycardia against apnoea duration. (C) The percentage change in oxygen saturation during apnoea compared with apnoea duration. (D) The change in cerebral haemoglobin difference (cHbD) during apnoea against apnoea duration. The size of the points indicates their weight in producing the linear best fit, calculated as the square root of the number of apnoea episodes associated with each point. ID: 2 – Gabriel et al. (1976) 17 , ID: 5 – Fenichel et al. (1980) 26 , ID: 6 – Vyas et al. (1981) 30 , ID: 9 - Henderson-Smart et al. (1986) 31 , ID: 11 – Mathew (1988) 32 , ID: 20 - Finer et al. (1992) 27 , ID: 28 – Urlesberger et al. (1999) 51 , ID: 29 – Carbone et al. (1999) 28 , ID: 31 – Pichler et al. (2003) 5 , ID: 36 – Beck et al. (2011) 29 , ID: 39– Marshall et al. (2019) 33 , ID: 42 – Varisco et al. (2022) 52 . Five studies 19 , 30 , 34 – 36 reported the percentage of different apnoea durations associated with a bradycardia event, defined as a decrease in heart rate to below 100 bpm; these were pooled in a separate meta-analysis. Gabriel et al. (1976) (b) 20 also reported this relationship, but it was not included in the meta-analysis as the data and results were the same as Gabriel et al. (1976) (a) 19 . Most studies showed that longer apnoeas were associated with higher rates of bradycardia (Supplementary Figure 2, n = 5 studies; slope = 1.71, R 2 = −0.05; r = 0.44, p = 0.0052). The study from Finer et al (1992) 30 was again removed from the meta-analysis due to its unique definition of apnoea. The pooled data from the other four studies showed a positive correlation between apnoea duration and the percentage of apnoeas associated with bradycardia ( Figure 2B , n = 4 studies, slope = 2.31, R 2 = 0.57; r = 0.81, p < 0.0001). The remaining studies (n = 12 studies) used a variety of heart rate metrics (e.g. absolute heart rate values during apnoea and multivariate linear analysis outcomes assessing the dependence of normalised percentage changes in heart rate on apnoea duration) and could not be pooled for additional meta-analysis due to limited data availability for each metric 21 , 37 – 47 ( Table 1 , Supplementary Table 1). The majority (n = 11 studies) demonstrated a statistically significant relationship between change in heart rate and apnoea duration. In contrast, Curzi-Dascalova et al. (1989) 48 studied 602 pauses in total, all lasting less than 12 seconds. Pauses of 10 to 12 seconds were rare (11 detected from all eligible babies) and did not induce higher cardiac deceleration than shorter pauses. 3.2 Relationship between apnoea duration and change in oxygen saturation A total of 24 articles investigated the relationship between change in oxygen saturation with apnoea duration ( Table 2 ), involving 1199 neonates and 1970 recording sessions in total. All the studies used a pulse oximeter to measure oxygen saturation. View this table: View inline View popup Table 2: Summary of included studies reporting the relationship between apnoea duration and change in oxygen saturation. SD: standard deviation. As with the studies investigating changes in heart rate, a wide variety of oxygen saturation metrics and statistical analysis techniques were used. Eight studies 4 , 24 , 27 , 30 , 49 – 52 reported correlations between apnoea duration and different oxygen saturation metrics. Six of these studies 4 , 24 , 27 , 30 , 49 , 50 reported significant correlation coefficients, all indicating that longer apnoea durations were associated with greater decreases in oxygen saturation. Four of these significant correlations 4 , 24 , 30 , 49 were based on the same oxygen saturation metric - the percentage decrease in oxygen saturation. The pooled correlation between these four correlation coefficients was 0.37 (n = 4 studies, p < 0.0001, 95% confidence interval: [0.34, 0.39]). In contrast, Poets et al. (1995) 51 reported no significant correlation between the duration of the apnoeic pause and the duration of desaturation (r = 0.3; P > 0.05, Spearman’s rank correlation test). Similarly, Adams et al. (1997) 52 found no significant correlation between the length of respiratory pauses and the rate of desaturations (r = −0.20). Five studies 5 , 30 , 31 , 53 , 54 presented the mean change in oxygen saturation during apnoea for different apnoea duration subgroups. The average of the mean PMA of participants in the five studies is 32.68 ± 2.48 weeks. Meta-analysis pooling the data from these studies showed a negative relationship between apnoea duration and change in oxygen saturation ( Figure 2C , n = 5 studies, slope = −0.52, R 2 = 0.95; r = − 0.98, p < 0.0001). Three studies 4 , 24 , 49 used linear regression models to relate apnoea duration and change in oxygen saturation. The regression line slope from the pooled data (−0.51) were within the range of these models (Supplementary Figure 3, −0.34 for Muttitt et al. (1988) 24 , −0.43 for Upton et al. (1991) 4 , −0.67 for Upton et al. (1992) 49 ). The remaining studies used a variety of oxygen saturation metrics (e.g. area under the curve, lowest recorded oxygen saturation, and duration of apnoeas with and without desaturation), demonstrating significant correlations between apnoea duration and oxygen saturation. The data could not be pooled for additional meta-analysis due to limited data availability for each metric 21 , 28 , 36 , 40 – 42 , 44 , 55 – 59 ( Table 2 , Supplementary Table 1). 3.3 Relationship between apnoea duration and change in cerebral oxygenation Three studies examined the relationship between apnoea duration and cerebral oxygenation ( Table 3 ). Changes in cerebral oxygenation were measured using Near Infra-Red Spectroscopy (NIRS). Jenni et al. (1996) 27 reported the correlation coefficients between the amplitude of total haemoglobin concentration (tHb) and duration of apnoea as 0.55 for central apnoea, −0.005 for obstructive apnoea, and 0.7 for mixed apnoea. Urlesberger et al. (1999) 53 and Pichler et al. (2003) 5 presented the mean change in cerebral haemoglobin difference (cHbD) during apnoea for different apnoea duration groups. Meta-analysis of the two studies demonstrated that cHbD decreased more during apnoeas with longer durations ( Figure 2D , n = 2 studies, slope = −0.35, R 2 = 0.83; r = −0.92, p < 0.0001). View this table: View inline View popup Table 3: Summary of included studies reporting the relationship between apnoea duration and change in cerebral oxygenation. tHb: total haemoglobin concentration. cHbD: cerebral haemoglobin oxygenation index. cHbtot: concentration changes of total cerebral haemoglobin. View this table: View inline View popup Download powerpoint Table 4: Summary of included studies reporting the relationship between apnoea duration and change in cerebral blood volume. 3.4 Relationship between apnoea duration and change in cerebral blood volume Only one study from Pichler et al. (2003) 5 reported the relationship between apnoea duration and cerebral blood volume ( Table 4 ). The changes in cerebral blood volume were measured using NIRS. The study population comprised two subgroups: a bradycardia group (bradycardia was defined as a heart rate decrease to below 80 beats per minute, n = 20) and a non-bradycardia group (n = 19). Episodes of apnoea with associated bradycardia were matched with episodes of apnoea without bradycardia based on the duration and PMA of the neonates. The cerebral blood volume decreased significantly with increased apnoea duration in the bradycardia group, but the relationship was not significant in the non-bradycardia group. 3.5 Factors which modulate the physiological response to apnoea While most studies showed that longer apnoea durations correspond to greater physiological changes, there is considerable variation, particularly in heart rate changes ( Figure 2 ). Studies included in this review have investigated whether apnoea type and the use of theophylline can affect the relationship between apnoea duration and changes in physiology. Using a meta-analysis, we also explored whether PMA modulates the relationship. 3.5.1 Apnoea type Several studies 24 , 25 , 27 showed that central and obstructive apnoeas could lead to different physiological responses. Muttitt et al. (1988) 24 identified a significant correlation between apnoea duration and heart rate decrease in central apnoeas (computer-diagnosed: r = 0.19, p < 0.0001; nurse-diagnosed: r = 0.20, p < 0.0001), but not in obstructive or mixed apnoea groups. Suichies et al. (1989) 25 reported significant positive correlations between the percentage of apnoeas associated with bradycardias and apnoea duration in central apnoeas (r = 0.65, p < 0.01) and mixed apnoeas (r = 56, p < 0.05), but the correlation was not significant for obstructive apnoeas. Similarly, Jenni et al. (1996) 27 found that the minimum heart rate during apnoea was negatively correlated with apnoea duration in central apnoeas (r = −0.77, p < 0.01) and mixed apnoeas (r = −0.69, p < 0.01) only, but again, no significant correlation was observed in obstructive apnoeas. However, with regard to oxygen saturation, Jenni et al. (1996) 27 observed a significant correlation between apnoea duration and oxygen saturation for all apnoea types (central: r = −0.49, p < 0.01; obstructive: r = −0.49, p < 0.05; mixed: r = −0.64, p < 0.01). Studies shown in Figure 2A included four data points associated with central apnoea, one data point associated with obstructive apnoea, and 16 data points involving any apnoea type. Meta-analysis on apnoea type was not performed for these studies due to limited data availability. The meta-analysis of the studies presented in Figures 2B and 2C indicated that apnoea type played a significant role in modulating the relationship between bradycardia incidence rate and apnoea duration (Supplementary Table 2), as well as between changes in oxygen saturation and apnoea duration (Supplementary Table 3). 3.5.2 Theophylline There is limited evidence for theophylline as a modulating factor. Muttitt et al. (1988) 24 found in a sub-group analysis that the correlation between apnoea duration and decrease in heart rate was significant in a theophylline-treated group (r = 0.16, p < 0.0001) but not in the untreated group. Theophylline did not significantly affect the decrease in oxygen saturation in this study. However, the correlation between apnoea duration and decrease in oxygen saturation was the strongest in the theophylline-treated group (r = 0.43, p < 0.0001). Similarly, Upton et al. (1991) 4 found that theophylline did not reduce the slope of the reduction in oxygen saturation for the duration of the apnoeic attack (treated: r = 0.45, p < 0.0001; untreated: r = 0.24, p < 0.0001). 3.5.3 Postmenstrual age While none of the included studies analysed the effect of age on neonates’ responses to apnoea, the meta-analysis of the studies shown in Figure 2C revealed that PMA significantly modulated the relationship between changes in oxygen saturation and apnoea duration (Supplementary Table 4). A post hoc analysis showed that for neonates with a PMA of 30, 32, 34, and 36 weeks, the predicted apnoea durations associated with a drop in oxygen saturation greater than 10% were 18.9, 20.0, 21.5, and 23.4 seconds, respectively (i.e. younger neonates have a significant drop in oxygen saturation with shorter apnoeas). In the mixed-effects model analysis comparing percentage change in heart rate with apnoea duration, PMA was not a significant factor (Supplementary Table 5). PMA was not incorporated into the other mixed-effects models due to a lack of information. 3.6 Quality assessment A summary of the outcomes of the quality assessments can be found in Tables 1 - 4 , and the detailed results from the JBI checklists are provided in Supplementary Tables 6-9. None of the papers satisfied all the requirements in the checklists. The results of overall risk of bias assessments can be found in Supplementary Table 10. Five studies (12.2%) had a low overall risk of bias, 21 studies (51.2%) had a moderate overall risk of bias, and 15 studies (36.6%) had a high overall risk of bias. Among the 15 studies included in the meta-analyses in Figure 2 and the combined correlation factors in Section 3.2 , 7 had a high risk of bias, 6 had a moderate risk, and 2 had a low risk. 4. Discussion This systematic review and meta-analysis investigated how the duration of apnoea, or pauses in breathing, correlates with changes in physiology in preterm neonates. Overall, the meta-analysis demonstrated that the minimum value and the extent of decrease in heart rate, oxygen saturation, and cerebral oxygenation during apnoea were significantly correlated with the duration of apnoea. This aligns with the outcomes of most studies included in this review. In contrast, a change in cerebral blood volume was only correlated with apnoea duration in the group of neonates with bradycardia, but not in the non-bradycardia group. However, this was only investigated in one study, and therefore the results require further validation. Although there is a strong relationship between changes in physiology and apnoea duration, there is nevertheless considerable variation ( Figure 2 ), with the R 2 values indicating that changes in heart rate exhibited greater variation than changes in oxygen saturation. In the meta-analysis, changes in physiological parameters were assessed as the percentage change during apnoea compared to a baseline period before the apnoea. As neonates generally have a baseline oxygen saturation above 90%, and clinical teams intervene if saturation decreases below 90% for a significant period, the differences in baselines across studies were minimal. In contrast, baseline heart rate can vary much more significantly between individual neonates, which might partly explain this difference in variation between the models. Moreover, differences between studies, such as in measurement techniques or ventilatory support may account for variation. In addition to variation across studies, there is considerable variation across individual apnoeas, as observed in the included papers, such as Upton et al. (1991) 4 . Significant and recurrent physiological fluctuations from apnoea may have clinical implications both acutely and long-term, with studies suggesting associations with increased incidence of ROP 7 and cognitive deficits later in life 8 , 9 . Understanding the factors which modulate the relationship between apnoea duration and changes in physiology could allow for the identification of neonates that are at particular risk from large changes in physiology following even short apnoeas, and conversely those neonates who remain physiologically stable. Apnoea type, the use of theophylline and the neonate’s PMA were identified as significant factors modulating the relationship between apnoea duration and changes in physiology. Through meta-analysis, we demonstrate that PMA significantly modulates the relationship between apnoea duration and oxygen saturation. This result suggests that different durations of breathing pause should be used as apnoea alarm limits for neonates with different PMA to reduce oxygen desaturations. In particular, we propose that for infants younger than 32 weeks PMA, an apnoea alarm limit of 15 seconds is considered. Investigation of other factors modulating the relationship was limited by the number of studies included in this review and the availability of data for each outcome examined. Insufficient data prevented analysis of factors such as pathological conditions, ventilation modes and medications other than theophylline. Future studies are warranted to identify other modulating factors. This study was limited by the small sample sizes of most included studies, which may introduce potential bias in the results. Additionally, the publication years of the studies were relatively old, with more than half published over 30 years ago. The methods of measurement and clinical techniques likely differ significantly from those used today. Only five papers showed a low overall risk of bias, while the remaining studies had either moderate or high risks of bias. Many papers had missing or unclear information on important topics. For example, less than half (40.9%) reported the sex of the neonates. There was significant inconsistency between papers in the definitions of apnoea, bradycardia, and other events. Furthermore, useful information was sometimes presented only in figures, requiring us to extract data points from those figures, which could lead to inaccuracies. Nevertheless, meta-analysis demonstrated consistent results across studies and provide a basis for future work investigating the relationship between apnoea duration and change in physiology. 5. Conclusion This systematic review and meta-analysis provides strong evidence that longer apnoea durations directly correlate with greater deterioration in cardiorespiratory and cerebral parameters in preterm neonates. Most critically, we identified postmenstrual age as a key determinant of physiological vulnerability – with younger infants more likely to experience oxygen desaturation from shorter apnoeas. Current standard monitor settings using 20-second thresholds for all preterm infants are likely inadequate for the most vulnerable babies. We recommend clinical implementation of PMA-stratified apnoea alarm thresholds, with a threshold of 15 seconds for infants <32 weeks PMA. Additionally, central apnoeas warrant closer monitoring than obstructive events due to their stronger association with physiological deterioration. These findings are readily translatable to clinical practice and should inform newborn care unit monitoring protocols to prevent potentially harmful physiological instability in our most vulnerable patients. Funding This work was funded by the Wellcome Trust and Royal Society through a Fellowship provided to CH (grant reference number: 213486/Z/18/Z). YC is funded by the Department of Paediatrics at the University of Oxford and the China Scholarship Council (CSC). Author Contributions YC contributed to the study design, screened the search results, extracted data, completed the quality assessment and drafted the initial manuscript. CH designed the study, provided supervision and screened the search results. CZ contributed to screening the search results and quality assessment. LBa, OF, VK, RP and ZS contributed to screening the search results. MH developed the search strategy. FU, LBe and MV provided mentorship and supervision. All authors critically reviewed and revised the manuscript. Competing interests There are no conflicts of interest. Consent statement Patient consent was not required. Codes availability Code for the meta-analysis can be found at: https://github.com/Yiru-C/apnoea_physiology_meta_analysis.git Data availability All extracted data are provided in Supplementary Table 1. Data Availability All extracted data are provided in Supplementary Table 1. References 1. ↵ Ohuma , E. O. et al. 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