Perfusion Index: A Noninvasive Tool for Clinical Decision in Neonates with Suspected Sepsis?

Perri, Alessandro; Barone, Giuseppe; Iannotta, Rossella; Bottoni, Anthea; Tiberi, Eloisa; Zecca, Enrico

doi:10.1055/s-0038-1649340

Cited by 2 publications

(6 citation statements)

References 23 publications

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“…In the current study comparing 148 neonates with proven/probable sepsis and 126 no‐sepsis, we observed comparable PI values in the two groups except that the neonates with proven/probable sepsis had significantly lower PI values in three time‐epochs (0–6 h, 31–36 h and 49–54 h) than neonates with no‐sepsis. These findings are in sync with the published literature 11,12,14 . The neonates born to mothers with histological chorioamnionitis had significantly lower PI values at 1 and 5 min of life than normal neonates 11,12 .…”

Section: Discussionsupporting

confidence: 89%

“…These findings are in sync with the published literature. 11,12,14 The neonates born to mothers with histological chorioamnionitis had significantly lower PI values at 1 and 5 min of life than normal neonates. Interestingly, non-survivors had significantly lower PI values even at the baseline.…”

Section: Discussionmentioning

confidence: 97%

“…However, PI did not show an independent association with in-hospital mortality on multivariable analysis. Although there are no conclusive studies in neonates, [12][13][14] some studies in older age groups have found PI to be an independent predictor of mortality in out-of-hospital cardiac arrest, 22 sepsis associated acute kidney injury. 23 Lack TA B L E 2 Perfusion index and plethysmography variability index values in neonates with proven/probable sepsis and no-sepsis.…”

Section: Discussionmentioning

confidence: 99%

“…The analysis was performed in SPSS v.22.0 (IBM). The sample size was based on published data by Perri et al 14 Considering a mean PI of 0.8 in sepsis group and 1.2 in non-sepsis group, a standard deviation of 1.0, an α-error of 0.05 and power of 90%; a total sample size of 274 was required. or probable sepsis [77 culture-proven and 71 probable sepsis]) and 126 no-sepsis (Figure 1).…”

Section: Statistical Analysis and Sample Sizementioning

confidence: 99%

“…These parameters may have the potential to inform mortality in sick patients 10 . PI has shown to be decreased in neonates born to mothers with chorioamnionitis, 11,12 neonates with sepsis 13,14 and shock 15 . PVI has been used to identify volume responsiveness in neonates and infants 16,17 .…”

Section: Introductionmentioning

confidence: 99%

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Role of perfusion index and plethysmography variability index for predicting outcomes in neonatal sepsis

et al. 2023

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Aim(i) To compare perfusion index (PI) and plethysmography variability index (PVI) between neonates with proven or probable sepsis versus no‐sepsis, (ii) to examine an association of PI and PVI with in‐hospital mortality.MethodsWe enrolled neonates with clinically presumed sepsis. Culture‐proven or probable sepsis were categorised as ‘cases’ and no‐sepsis as ‘controls’. PI and PVI were recorded hourly for 120 h and averaged in 20‐time epochs (0–6 h to 115–120 h).ResultsWe analysed 148 neonates with sepsis (proven sepsis = 77, probable sepsis = 71) and 126 with no‐sepsis. Neonates with proven/probable sepsis and no‐sepsis had comparable PI and PVI values. Among 148 neonates with sepsis, 43 (29%) died. Non‐survivors had significantly lower PI values than survivors (mean difference 0.21 [95% CI 0.14–0.29], p‐value <0.001). PI had a significant but modest discriminative ability to identify non‐survivors. However, PI did not independently predict mortality.ConclusionNeonates with proven/probable sepsis and no‐sepsis had comparable PI and PVI values in the first 120 h of sepsis. PI but not PVI values were significantly lower in non‐survivors than survivors. PI did not independently predict in‐hospital mortality. Due to modest discriminative ability, PI should be interpreted along with other vital signs to take clinical decisions.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Statistical Analysis and Sample Sizementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Role of perfusion index and plethysmography variability index for predicting outcomes in neonatal sepsis

et al. 2023

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Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System

Garstman,

Rodriguez Rivero,

Onland

2023

Applied Sciences

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A significant proportion of babies that are admitted to the neonatal intensive care unit (NICU) suffer from late onset sepsis (LOS). In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage. Some works have shown that linear methods (i.e., logistic regression) display a superior performance when predicting LOS. Nevertheless, as research on this topic is still in an early phase, it has not been ruled out that non-linear machine learning (ML) techniques can improve the predictive performance. Moreover, few studies have assessed the effect of parameters other than heart rate variability (HRV). Therefore, the current study investigates the effect of non-linear methods and assesses whether other vital parameters such as respiratory rate, perfusion index, and oxygen saturation could be of added value when predicting LOS. In contrast with the findings in the literature, it was found that non-linear methods showed a superior performance compared with linear models. In particular, it was found that random forest performed best (AUROC: 0.973), 24% better than logistic regression (AUROC: 0.782). Nevertheless, logistic regression was found to perform similarly to some non-linear models when trained with a short training window. Furthermore, when also taking training time into account, K-Nearest Neighbors was found to be the most beneficial (AUROC: 0.950). In line with the literature, we found that training the models on HRV features yielded the best results. Lastly, the results revealed that non-linear methods demonstrated a superior performance compared with linear methods when adding respiratory features to the HRV feature set, which ensured the greatest improvement in terms of AUROC score.

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Perfusion Index: A Noninvasive Tool for Clinical Decision in Neonates with Suspected Sepsis?

Cited by 2 publications

References 23 publications

Role of perfusion index and plethysmography variability index for predicting outcomes in neonatal sepsis

Role of perfusion index and plethysmography variability index for predicting outcomes in neonatal sepsis

Early Detection of Late Onset Sepsis in Extremely Preterm Infants Using Machine Learning: Towards an Early Warning System

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