Risk prediction models for enteral nutrition-related aspiration in critically ill patients: A systematic review

Table 1 Characteristics of included studies (n = 12)

Ref.	Country	Study objective	Data source	Study design	Study population
Chen et al[9], 2025	China	Model development	Single center	Retrospective cohort	ICU patients receiving EN
Zhou et al[17], 2025	China	Model development	Single center	Prospective cohort	Critically ill stroke patients
Shi et al[14], 2024	China	Development and validation	Single center	Retrospective cohort	Postoperative glioma patients in ICU
Peng et al[13], 2022	China	Development and validation	Single center	Retrospective cohort	Critically ill ICH patients
Guan et al[10], 2022	China	Development and validation	Single center	Prospective cohort	Severe acute pancreatitis
Jing and Wu[11], 2023	China	Development and validation	Single-center	Prospective cohort	Tube-fed patients
Luo et al[12], 2024	China	Development and validation	Single-center	Retrospective cohort	Mechanically ventilated children
Yu et al[15], 2022	China	Model development	Single-center	Retrospective cohort	Severe traumatic brain injury
Zhang et al[16], 2022	China	Development and validation	Single-center	Retrospective cohort	Patients receiving nasogastric feeding
Sun et al[19], 2020	China	Development and validation	Single-center	Prospective case-control	Patients receiving nasogastric feeding
Wang et al[20], 2024	China	Development and validation	Single-center	Prospective cohort	Acute ischemic stroke patients
Hou et al[18], 2024	China	Development and validation	Single-center	Retrospective cohort	Acute pancreatitis patients

ICU: Intensive care unit; EN: Enteral nutrition; ICH: Intracerebral hemorrhage.

Table 2 Characteristics of prediction models for enteral nutrition-related aspiration in critically ill patients (n = 12)

Ref.	Total sample	No. of candidate variables	Aspiration incidence, %	Modeling method	Model presentation	AUC	Sensitivity	Specificity	Validation method	No. of predictors	Predictor variables
Chen et al[9]	500	28	57	Logistic regression	Nomogram	0.82	0.776	0.695	Internal	6	Intubation days, body position, daily EN duration, APACHE II score, sedatives/analgesics, PaO2
Zhou et al[17]	60	8	46.67	Logistic regression						4	Age, impaired consciousness, dysphagia, gastrointestinal dysmotility
Shi et al[14]	379	12	19.44	Logistic regression		0.771			Internal	3	COPD, duration of mechanical ventilation, duration of postoperative coma
Peng et al[13]	368	21	39.95	R software	Nomogram	0.995	0.952	0.842	Internal and external	5	NG tube diameter, gastric residual volume, history of aspiration, NIHSS score, Water Swallow Test grade
Guan et al[10]	296	15	9.46	R software, Python	Random forest, neural network, decision tree, support vector machine, generalized linear regression	0.976			Internal	5	APACHE II score, level of consciousness, nutritional risk, NG tube insertion depth, PLR
Jing and Wu[11]	103	29	20.08	R software	Nomogram				Internal	4	Number of comorbidities, intubation depth, history of aspiration, sedatives/hypnotics
Luo et al[12]	330	13	31.52	R software	Nomogram	0.810			Internal and external	7	Gastric residual volume, mode of mechanical ventilation, feeding volume, level of consciousness, NG tube depth, prokinetics, sedatives
Yu et al[15]	212	11	48.11	Logistic regression	Nomogram				Internal	6	Age, diabetes mellitus, APACHE II score, impaired consciousness, nutritional risk, NG tube length
Zhang et al[16]	220	27	20.9	Logistic regression, R software	Nomogram, CART	0.895, 0.902	0.825, 0.806	0.736, 0.758	Internal	11	Age, history of aspiration, number of comorbidities, NG tube depth, NG tube duration, food source, sedatives/hypnotics, impaired consciousness, complications, serum CRP, serum albumin
Sun et al[19]	515	29	20	Logistic regression, R software	Nomogram, CART	0.93, 0.96	0.909, 0.883	0.886, 0.962	Internal	5	History of aspiration, number of comorbidities, intubation depth, sedatives/hypnotics
Wang et al[20]	359	30	16.9	Logistic regression	Nomogram	0.853			Internal and external	4	Suctioning, brainstem infarction, temporal lobe infarction, Barthel Index score
Hou et al[18]	200	11	12.5	Logistic regression	Nomogram	0.926	0.884	0.852	Internal	5	Body position, level of consciousness, nutritional risk, APACHE II score, NG tube length

APACHE II: Acute Physiology and Chronic Health Evaluation II; EN: Enteral nutrition; COPD: Chronic obstructive pulmonary disease; NG: Nasogastric; NIHSS: National Institutes of Health Stroke Scale; PLR: Platelet-to-lymphocyte ratio; CART: Classification and regression trees; CRP: C-reactive protein; AUC: Area under the receiver operating characteristic curve.

Table 3 Risk of bias and applicability assessment using Prediction model Risk of Bias Assessment Tool (n = 12)

Ref.	Participants	Predictors	Outcome	Analysis	Overall risk of bias	Participants	Predictors	Outcome	Overall applicability
Chen et al[9]	H	H	L	H	H	L	L	L	L
Zhou et al[17]	L	L	L	H	H	L	L	L	L
Shi et al[14]	H	H	L	H	H	L	L	L	L
Peng et al[13]	H	H	L	H	H	L	L	L	L
Guan et al[10]	L	?	L	H	H	L	L	L	L
Jing and Wu[11]	L	L	L	H	H	L	L	L	L
Luo et al[12]	H	H	L	H	H	L	L	L	L
Yu et al[15]	H	?	L	H	H	L	L	L	L
Zhang et al[16]	H	H	L	L	H	L	L	L	L
Sun et al[19]	L	L	L	L	L	L	L	L	L
Wang et al[20]	L	L	L	L	L	L	L	L	L
Hou et al[18]	H	H	L	H	H	L	L	L	L

L: Low risk of bias/good applicability; H: High risk of bias/poor applicability; “?”: Unclear risk of bias.