Prediction and prognosis of adverse maternal and foetal/neonatal outcomes in pulmonary hypertension: an observational study and nomogram construction

Background Pregnant women with pulmonary hypertension (PH) have higher mortality rates and poor foetal/neonatal outcomes. Tools to assess these risk factors are not well established. Methods Predictive and prognostic nomograms were constructed using data from a “Development” cohort of 420 pregnant patients with PH, recorded between January 2009 and December 2018. Logistic regression analysis established models to predict the probability of adverse maternal and foetal/neonatal events and overall survival by Cox analysis. An independent “Validation” cohort comprised data of 273 consecutive patients assessed from January 2019 until May 2022. Nomogram performance was evaluated internally and implemented with online software to increase the ease of use. Results Type I respiratory failure, New York Heart Association functional class, N-terminal pro-brain natriuretic peptide $$\ge$$ 1400 ng/L, arrhythmia, and eclampsia with pre-existing hypertension were independent risk factors for maternal mortality or heart failure. Type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, New York Heart Association functional class, and N-terminal pro-brain natriuretic peptide $$\ge$$ 1400 ng/L were independent predictors of pulmonary hypertension survival during pregnancy. For foetal/neonatal adverse clinical events, type I respiratory failure, arrhythmia, general anaesthesia for caesarean section, parity, platelet count, fibrinogen, and left ventricular systolic diameter were important predictors. Nomogram application for the Development and Validation cohorts showed good discrimination and calibration; decision curve analysis demonstrated their clinical utility. Conclusions The nomogram and its online software can be used to analyse individual mortality, heart failure risk, overall survival prediction, and adverse foetal/neonatal clinical events, which may be useful to facilitate early intervention and better survival rates..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:23

Enthalten in:

Respiratory research - 23(2022), 1 vom: 15. Nov.

Sprache:

Englisch

Beteiligte Personen:

Chen, Yuqin [VerfasserIn]
Zhou, Dansha [VerfasserIn]
Xiong, Mingmei [VerfasserIn]
Xi, Xin [VerfasserIn]
Zhang, Wenni [VerfasserIn]
Zhang, Ruifeng [VerfasserIn]
Chen, Lishi [VerfasserIn]
Jiang, Qian [VerfasserIn]
Lai, Ning [VerfasserIn]
Li, Xiang [VerfasserIn]
Luo, Jieer [VerfasserIn]
Li, Xuanyi [VerfasserIn]
Feng, Weici [VerfasserIn]
Gao, Chuhui [VerfasserIn]
Chen, Jiyuan [VerfasserIn]
Fu, Xin [VerfasserIn]
Hong, Wei [VerfasserIn]
Jiang, Mei [VerfasserIn]
Yang, Kai [VerfasserIn]
Lu, Wenju [VerfasserIn]
Luo, Yiping [VerfasserIn]
Zhang, Jun [VerfasserIn]
Cheng, Zhe [VerfasserIn]
Liu, Chunli [VerfasserIn]
Wang, Jian [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

Machine-based model
Nomogram
Overall survival
Prediction model
Pregnancy
Prognostic model
Pulmonary hypertension

Anmerkungen:

© The Author(s) 2022. corrected publication 2022

doi:

10.1186/s12931-022-02235-y

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2132728720