Vital signs analysis algorithm detects inflammatory response in premature infants with late onset sepsis and necrotizing enterocolitis

Urinary tract infections in very premature neonates: the definition dilemma

BACKGROUND AND OBJECTIVES

Data on urinary tract infections (UTIs) in very preterm neonates (VPTNs) are scarce. We aimed to (i) describe the characteristics of UTIs in VPTNs and (ii) compare the diagnostic practices of neonatal clinicians to established pediatric guidelines.

METHODS

All VPTNs (<29 weeks GA) with a suspected UTI at the CHU Sainte-Justine neonatal intensive care unit from January 1, 2014, and December 31, 2019, were included and divided into two definition categories: Possible UTI, and Definite UTI.

RESULTS

Most episodes were Possible UTI (87%). Symptoms of UTIs and pathogens varied based on the definition category. A positive urinalysis was obtained in 25%. Possible UTI episodes grew 2 organisms in 62% of cases and <50,000 CFU/mL in 62% of cases.

CONCLUSION

Characteristics of UTIs in VPTNs vary based on the definition category and case definitions used by clinicians differ from that of established pediatric guidelines.

Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis

OBJECTIVE

To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU).

STUDY DESIGN

Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation).

RESULTS

The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set.

CONCLUSIONS

Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis

BACKGROUND AND AIM

Preterm infants are prone to neonatal infections such as late-onset sepsis (LOS). The consequences of LOS can be severe and potentially life-threatening. Unfortunately, LOS often presents with unspecific symptoms, and early screening laboratory tests have limited diagnostic value and are often late. This study aimed to build a predictive algorithm to aid doctors in the early detection of LOS in very preterm infants.

METHODS

In a retrospective cohort study, all consecutively admitted preterm infants (GA ≤ 32 weeks) from 2008 until 2019 were included. They were classified as LOS or control according to blood culture results, currently the gold standard. To generate features, routine and continuously measured oxygen saturation and heart rate data with a minute-by-minute sampling rate were extracted from electronic medical records. Care was taken not to include variables indicative of existing LOS suspicion. The timing of a positive blood culture served as a proxy for LOS-onset. An equivalent timestamp was generated in gestational-age-matched control patients without a positive blood culture. Three machine learning (ML) techniques (generalized additive models, logistic regression, and XGBoost) were used to build a classification algorithm. To simulate the performance of the algorithm in clinical practice, a simulation using multiple alarm thresholds was performed on hourly predictions for the total hospitalization period.

RESULTS

292 infants with LOS were matched to 1497 controls. The median gestational age before matching was 28.1 and 30.3 weeks, respectively. Evaluation of the overall discriminative power of the LR algorithm yielded an AUC of 0.73 (p < 0.05) at the moment of clinical suspicion (t = 0). In the longitudinal simulation, our algorithm detects LOS in at least 47% of the patients before clinical suspicion without exceeding the alarm fatigue threshold of 3 alarms per day. Furthermore, medical experts evaluated the algorithm as clinically relevant regarding the feature contributions in the model explanations.

CONCLUSIONS

An ML algorithm was trained for the early detection of LOS. Performance was evaluated on both prediction horizons and in a clinical impact simulation. To the best of our knowledge, our assessment of clinical impact with a retrospective simulation on longitudinal data is the most extensive in the literature on LOS prediction to date. The clinically relevant algorithm, based on routinely collected data, can potentially accelerate clinical decisions in the early detection of LOS, even with limited inputs.

Vital sign-based detection of sepsis in neonates using machine learning

AIM

Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis.

METHODS

Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion.

RESULTS

Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold.

CONCLUSION

The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

Antibiotic Resistance and Biofilm Infections in the NICUs and Methods to Combat It

Neonatal sepsis is an important cause of neonatal morbidity and mortality. A significant proportion of bacteria causing neonatal sepsis is resistant to multiple antibiotics, not only to the usual empirical first-line regimens, but also to second- and third-line antibiotics in many neonatal intensive care units (NICUs). NICUs have unique antimicrobial stewardship goals. Apart from antimicrobial resistance, NICUs have to deal with another problem, namely biofilm infections, since neonates often have central and peripheral lines, tracheal tubes and other foreign bodies for a prolonged duration. The aim of this review is to describe traditional and novel ways to fight antibiotic-resistant bacteria and biofilm infections in NICUs. The topics discussed will include prevention and control of the spread of infection in NICUs, as well as the wise use of antimicrobial therapy and ways to fight biofilm infections.

Standardising the elusive diagnosis of NEC in the premature infant - A practical score

OBJECTIVE

The perceived risk of necrotising enterocolitis (NEC) can result in overtreatment of the otherwise adapting preterm neonate. We aim to develop an assessment tool to aid the decision making in the management of preterm neonates at risk of NEC.

METHOD

An evidence-based assessment tool was designed bringing together clinical, laboratory and radiological signs commonly associated with NEC. A numerical score was awarded for each sign, with those more specific to NEC being graded higher. A multi-centre validation was conducted of the proposed assessment tool over three tertiary neonatal units.

RESULTS

A total of 125 patients were included, 53 (42.4 %) with a final diagnosis of NEC and 72 (57.6 %) with an alternative diagnosis. The NEC group had a significantly higher total score compared to the non-NEC group; 15(2-28) vs. 4(1-9) (p ≤ 0.0001). In ROC analysis, using a cut-off of eight, the assessment tool gave a sensitivity of 92.3 % and a specificity of 90.4 % for identifying NEC compared to an alternative diagnosis.

CONCLUSION

This comprehensive scoring system encourages a full assessment of the infant before deciding on withholding feeds, starting antibiotics, and transferring to a surgical centre. It is a safe objective measure to support a diagnosis of NEC in the presence of certain clinical signs.

Intravenous Ceftazidime-Avibactam in Extremely Premature Neonates With Carbapenem-Resistant Enterobacteriaceae: Two Case Reports

The increasing use of carbapenems has contributed to a notable distribution of carbapenem-resistant Enterobacteriaceae (CRE). Recently, the incidence of CRE-associated infections is increasing significantly in NICUs, which pose a grave challenge to clinical treatment. We report 2 cases of IV ceftazidimeavibactam use to treat CRE infections in extremely premature neonates. The first case was diagnosed with bacteraemia and meningitis and the second one was diagnosed with bacteraemia only. Due to the lack of neonatal-specific information for IV ceftazidime-avibactam, the usual pediatric dose (62.5 mg/kg/dose every 8 hours) was used in these patients. Clinical cure occurred in these 2 patients. Although blood cultures became sterile after starting ceftazidime-avibactam in the second case, the patient died, presumably owing to sepsis or various causes, such as prematurity and chronic lung disease. Large and randomized studies are necessary to ensure the safety and efficacy of IV ceftazidime-avibactam for the treatment of neonates with sepsis caused by multidrug resistant organisms.

Development of a Novel Assessment Tool and Code Sepsis Checklist for Neonatal Late-Onset Sepsis

BACKGROUND

Accurate diagnosis and timely management of neonatal late-onset sepsis (nLOS) have been less well-studied than those of early-onset sepsis. We noticed a delay in nLOS detection and management in our neonatal intensive care unit.

PURPOSE

To develop an assessment tool to aid in the recognition and reporting of nLOS and to standardize the management process once sepsis is recognized.

METHODS

The Plan-Do-Study-Act (PDSA) improvement model provided the framework for interventions for our antibiotic stewardship program, including the aims of this project. A literature review was performed to evaluate tools and other literature available to guide the evaluation and management of suspected sepsis. A quality improvement project was initiated to develop tools for the detection and management of nLOS.

RESULTS

An nLOS assessment tool to help identify neonates at risk for nLOS and a Code Sepsis checklist to standardize the process of evaluation and management of nLOS were developed. The guiding principles of this tool development were empowerment of nurses to initiate the assessment process, clarification of team roles, and removal of barriers to appropriate antibiotic administration.

IMPLICATIONS FOR PRACTICE

Useful and practical tools valued by nursing and the multidisciplinary team may facilitate timely identification and treatment of infants with nLOS.

IMPLICATIONS FOR RESEARCH

Future directions include validation of the nLOS assessment tool and the Code Sepsis checklist as well as ensuring the reliability of the tool to improve detection of nLOS and to reduce time to administer antibiotics in cases of nLOS.

Diagnosis of neonatal sepsis: the past, present and future

Sepsis remains a significant cause of neonatal mortality and morbidity, especially in low- and middle-income countries. Neonatal sepsis presents with nonspecific signs and symptoms that necessitate tests to confirm the diagnosis. Early and accurate diagnosis of infection will improve clinical outcomes and decrease the overuse of antibiotics. Current diagnostic methods rely on conventional culture methods, which is time-consuming, and may delay critical therapeutic decisions. Nonculture-based techniques including molecular methods and mass spectrometry may overcome some of the limitations seen with culture-based techniques. Biomarkers including hematological indices, cell adhesion molecules, interleukins, and acute-phase reactants have been used for the diagnosis of neonatal sepsis. In this review, we examine past and current microbiological techniques, hematological indices, and inflammatory biomarkers that may aid sepsis diagnosis. The search for an ideal biomarker that has adequate diagnostic accuracy early in sepsis is still ongoing. We discuss promising strategies for the future that are being developed and tested that may help us diagnose sepsis early and improve clinical outcomes. IMPACT: Reviews the clinical relevance of currently available diagnostic tests for sepsis. Summarizes the diagnostic accuracy of novel biomarkers for neonatal sepsis. Outlines future strategies including the use of omics technology, personalized medicine, and point of care tests.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

AIM

To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates.

METHODS

A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE.

PROSPERO ID

CRD42020205143.

RESULTS

After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results.

CONCLUSION

Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.

Safety and efficacy of colistin and fluoroquinolone in neonatal persistent late-onset sepsis

Growing resistance of microorganisms to antibiotics for the treatment of late-onset sepsis (LOS) in premature infants has led physicians to use antibiotics that are not well studied in neonatal populations. We aimed to determine the efficacy and safety of colistin and fluoroquinolone for the treatment of persistent LOS. We retrospectively reviewed infants with gram-negative LOS, who received either colistin or fluoroquinolone therapy, to determine if there was a significant difference in kidney and liver function tests and electrolyte levels before, during, and at the end of the treatment. Infants who received colistin and fluoroquinolone had 17 and 34 positive cultures with gram-negative organisms, respectively. Multi-drug resistant organisms were more common in infants who received colistin than in those who received fluoroquinolone. Microbiological clearance was found to be higher in infants treated with fluoroquinolone than in those treated with colistin. In both the groups, the median levels of kidney and liver function tests and electrolytes showed a significant increase during the treatment. The prescription of colistin and fluoroquinolones should be reserved for cases with no other safe and effective alternatives.

Platelets and Immature Neutrophils in Preterm Infants with Feeding Intolerance

OBJECTIVE

Feeding intolerance (FI) is a common presentation of necrotizing enterocolitis (NEC) and sepsis. NEC and sepsis are associated with hematological changes, but these changes alone are not reliable biomarkers for early diagnosis. This study examined whether the combination of hematological indices and FI can be used as an early diagnostic tool for NEC or sepsis.

STUDY DESIGN

This retrospective cohort study included infants born at <1,500 g or <30 weeks who had symptoms of FI. The exclusion criteria were congenital or chromosomal disorders, thrombocytopenia or platelet transfusion before the onset of FI, and history of bowel resection. We compared the hematological indices from infants with pathologic FI (due to NEC or sepsis) to infants with benign FI.

RESULTS

During the study period, 211 infants developed FI; 185 met the inclusion criteria. Infants with pathologic FI (n = 90, 37 cases with NEC and 53 with sepsis) had lower birth gestational age and weight compared with 95 infants with benign FI (n = 95). Pathologic FI was associated with lower platelet count (median 152 × 10³/μL vs. 285 × 10³/μL, p < 0.001) and higher immature-to-total neutrophil (I/T) ratio (median 0.23 vs. 0.04, p < 0.001) at the onset of FI. Pathologic FI was also associated with a decrease in baseline platelets compared with an increase in benign FI. For diagnosis of pathologic FI, a decrease ≥10% in platelets from baseline had a sensitivity and specificity of 0.64 and 0.73, respectively, I/T ratio ≥0.1 had a sensitivity and specificity of 0.71 and 0.78, respectively, and the combination of both parameters had a sensitivity and specificity of 0.50 and 0.97, respectively.

CONCLUSION

FI caused by NEC or sepsis was associated with a decrease in platelets from baseline, and a lower platelet level and higher I/T ratio at the onset of FI. These findings can help clinicians in the management of preterm infants with FI.

KEY POINTS

· FI is a common presentation of NEC and sepsis in preterm infants.. · FI due to NEC or sepsis is associated with changes in platelets and I/T ratio.. · These changes could be useful as early markers for diagnosis..

Vital sign metrics of VLBW infants in three NICUs: implications for predictive algorithms

BACKGROUND

Continuous heart rate (HR) and oxygenation (SpO₂) metrics can be useful for predicting adverse events in very low birth weight (VLBW) infants. To optimize the utility of these tools, inter-site variability must be taken into account.

METHODS

For VLBW infants at three neonatal intensive care units (NICUs), we analyzed the mean, standard deviation, skewness, kurtosis, and cross-correlation of electrocardiogram HR, pulse oximeter pulse rate, and SpO₂. The number and durations of bradycardia and desaturation events were also measured. Twenty-two metrics were calculated hourly, and mean daily values were compared between sites.

RESULTS

We analyzed data from 1168 VLBW infants from birth through day 42 (35,238 infant-days). HR and SpO₂ metrics were similar at the three NICUs, with mean HR rising by ~10 beats/min over the first 2 weeks and mean SpO₂ remaining stable ~94% over time. The number of bradycardia events was higher at one site, and the duration of desaturations was longer at another site.

CONCLUSIONS

Mean HR and SpO₂ were generally similar among VLBW infants at three NICUs from birth through 6 weeks of age, but bradycardia and desaturation events differed in the first 2 weeks after birth. This highlights the importance of developing predictive analytics tools at multiple sites.

IMPACT

HR and SpO₂ analytics can be useful for predicting adverse events in VLBW infants in the NICU, but inter-site differences must be taken into account in developing predictive algorithms. Although mean HR and SpO₂ patterns were similar in VLBW infants at three NICUs, inter-site differences in the number of bradycardia events and duration of desaturation events were found. Inter-site differences in bradycardia and desaturation events among VLBW infants should be considered in the development of predictive algorithms.

Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability

OBJECTIVE

This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning.

METHODS

The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC).

RESULTS

The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics.

CONCLUSION

These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns.

SIGNIFICANCE

The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.

Diagnosis of Neonatal Late-Onset Infection in Very Preterm Infant: Inter-Observer Agreement and International Classifications

Background: The definition of late-onset bacterial sepsis (LOS) in very preterm infants is not unified. The objective was to assess the concordance of LOS diagnosis between experts in neonatal infection and international classifications and to evaluate the potential impact on heart rate variability and rate of “bronchopulmonary dysplasia or death”. Methods: A retrospective (2017–2020) multicenter study including hospitalized infants born before 31 weeks of gestation with intention to treat at least 5-days with antibiotics was performed. LOS was classified as “certain or probable” or “doubtful” independently by five experts and according to four international classifications with concordance assessed by Fleiss’s kappa test. Results: LOS was suspected at seven days (IQR: 5–11) of life in 48 infants. Following expert classification, 36 of them (75%) were considered as “certain or probable” (kappa = 0.41). Following international classification, this number varied from 13 to 46 (kappa = −0.08). Using the expert classification, “bronchopulmonary dysplasia or death” occurred less frequently in the doubtful group (25% vs. 78%, p < 0.001). Differences existed in HRV changes between the two groups. Conclusion: The definition of LOS is not consensual with a low international and moderate inter-observer agreement. This affects the evaluation of associated organ dysfunction and prognosis.

Non-Contact Video-Based Neonatal Respiratory Monitoring

Respiratory rate (RR) has been shown to be a reliable predictor of cardio-pulmonary deterioration, but standard RR monitoring methods in the neonatal intensive care units (NICU) with contact leads have been related to iatrogenic complications. Video-based monitoring is a potential non-contact system that could improve patient care. This iterative design study developed a novel algorithm that produced RR from footage analyzed from stable NICU patients in open cribs with corrected gestational ages ranging from 33 to 40 weeks. The final algorithm used a proprietary technique of micromotion and stationarity detection (MSD) to model background noise to be able to amplify and record respiratory motions. We found significant correlation—r equals 0.948 (p value of 0.001)—between MSD and the current hospital standard, electrocardiogram impedance pneumography. Our video-based system showed a bias of negative 1.3 breaths and root mean square error of 6.36 breaths per minute compared to standard continuous monitoring. Further work is needed to evaluate the ability of video-based monitors to observe clinical changes in a larger population of patients over extended periods of time.

Performance of Pediatric Systemic Inflammatory Response Syndrome and Organ Dysfunction Criteria in Late-Onset Sepsis in a Quaternary Neonatal Intensive Care Unit: A Case-Control Study

OBJECTIVES

To evaluate accuracy of systemic inflammatory response syndrome (SIRS) criteria in identifying culture-proven late-onset neonatal sepsis and to assess prevalence of organ dysfunction and its relationship with SIRS criteria.

STUDY DESIGN

This was a retrospective case-control study of patients in the Children's Hospital of Philadelphia level IV neonatal intensive care unit undergoing sepsis evaluations (concurrent blood culture and antibiotics). During calendar years 2016-2017, 77 case and 77 control sepsis evaluations were identified. Cases included infants who had sepsis evaluations with positive blood cultures and antibiotic duration ≥7 days. Controls were matched by gestational and postmenstrual age, and had sepsis evaluations with negative blood cultures and antibiotic duration ≤48 hours. SIRS criteria were determined at time of sepsis evaluation, and organ dysfunction evaluated in the 72 hours following sepsis evaluation. Statistical analysis included descriptive statistics, Mann-Whitney tests, and χ² (Fisher exact) tests.

RESULTS

At time of sepsis evaluation, 42% of cases and 26% of controls met SIRS criteria. Among infants of ≤37 weeks postmenstrual age, SIRS criteria were met in only 17% of sepsis evaluations (4 of 23 in both cases and controls). Test characteristics for SIRS at diagnosis of culture-proven sepsis included sensitivity 42% and specificity 74%. Cases had higher rates of new organ dysfunction within 72 hours (40% vs 21%); however, 58% of cases developing organ dysfunction did not meet SIRS criteria at time of sepsis evaluation. Of 6 deaths (all cases with organ dysfunction), 2 did not meet SIRS criteria at sepsis evaluation.

CONCLUSIONS

SIRS criteria did not accurately identify culture-proven late-onset sepsis, with poorest accuracy in preterm infants. SIRS criteria did not predict later organ dysfunction or mortality.

Predicting Neonatal Sepsis Using Features of Heart Rate Variability, Respiratory Characteristics, and ECG-Derived Estimates of Infant Motion

This study in preterm infants was designed to characterize the prognostic potential of several features of heart rate variability (HRV), respiration, and (infant) motion for the predictive monitoring of late-onset sepsis (LOS). In a neonatal intensive care setting, the cardiorespiratory waveforms of infants with blood-culture positive LOS were analyzed to characterize the prognostic potential of 22 features for discriminating control from sepsis-state, using the Naïve Bayes algorithm. Historical data of the subjects acquired from a period sufficiently before the clinical suspicion of LOS was used as control state, whereas data from the 24 h preceding the clinical suspicion of LOS were used as sepsis state (test data). The overall prognostic potential of all features was quantified at three-hourly intervals for the period corresponding to test data by calculating the area under the receiver operating characteristics curve. For the 49 infants studied, features of HRV, respiration, and movement showed characteristic changes in the hours leading up to the clinical suspicion of sepsis, namely, an increased propensity toward pathological heart rate decelerations, increased respiratory instability, and a decrease in spontaneous infant activity, i.e., lethargy. While features characterizing HRV and respiration can be used to probe the state of the autonomic nervous system, those characterizing movement probe the state of the motor system-dysregulation of both reflects an increased likelihood of sepsis. By using readily interpretable features derived from cardiorespiratory monitoring, opportunities for pre-emptively identifying and treating LOS can be developed.

Continuous pulse oximetry and respiratory rate trends predict short-term respiratory and growth outcomes in premature infants

BACKGROUND

To examine the correlation between interval vital signs recorded by nursing staff and continuous monitor recordings, and to determine whether aggregated monitor recordings can better predict impending escalation of respiratory support in premature infants.

METHOD

Preterm infants on noninvasive respiratory support or room air (RA) were prospectively enrolled. Nursing-and monitor-recorded pulse oximetry (SpO2) and respiratory rates (RR) data were recorded daily.

RESULTS

Ninety four infants were recruited with median gestational age of 32 weeks and birth weight of 1848 g. > 3 × 10⁶ data points were analyzed over 2204 patient days. Median events/day recorded was 8 (nursing) and 1424 (monitor) per infant. We did not find a strong correlation between monitor- and nursing events of tachypnea (RR > 70) and hypoxia (SpO2 < 90%). Infants with monitor-recorded hypoxia for > 5%/day (p < 0.0001) or tachypnea for > 30%/day (p < 0.0001) were more likely to require an increase in respiratory support within next 3 days. Monitor-recorded hypoxia and tachypnea were also associated with poor weight gain.

CONCLUSIONS

Monitor-recorded trends for tachypnea and oxygen saturations < 90% were able to predict short-term respiratory outcomes, and were associated with growth outcomes. This study emphasizes the potential for monitor-recorded data to augment clinical decision making at the bedside.