Background The effect of comorbid hypertriglyceridemia (HTG) and abdominal obesity (AO) on acute pancreatitis (AP) remains unclear. The aim of this study was to explore the effect of comorbid HTG and AO and discuss which is the dominant disorder. Methods In this study, 1219 AP patients who presented with HTG or AO were stratified into four groups: non-HTG + non-AO, HTG + non-AO, non-HTG + AO, and HTG + AO. Results The 328 patients with comorbid HTG + AO were much younger (42.29 ± 11.77), mainly male (79.57%), and had higher TG levels, larger waist circumferences, and more past medical histories than the patients in the other three non-comorbid groups (P < 0.001). The comorbidity group developed more incidences of persistent organ failure and local complications (P < 0.05). Multivariate logistic regression analysis showed that AO (OR = 3.205, 95% CI = 1.570–6.544), mild HTG (OR = 2.746, 95% CI = 1.125–6.701), and moderate to very severe HTG (OR = 3.649, 95% CI = 1.403–9.493) were independent risk factors for persistent respiratory failure (P < 0.05). Age > 60 years (OR = 1.326, 95% CI = 1.047–1.679), AO (OR = 1.701, 95% CI = 1.308–2.212), diabetes mellitus (OR = 1.551, 95% CI = 1.063–2.261), mild HTG (OR = 1.549, 95% CI = 1.137–2.112), and moderate to very severe HTG (OR = 2.810, 95% CI = 1.926–4.100) were independent risk factors associated with local complications (P < 0.05). Moreover, HTG seemed to be more dangerous than AO. The higher the serum TG level was, the greater the likelihood of persistent respiratory failure and local complications. Conclusions Comorbid HTG and AO will aggravate the severity and increase the incidence of local complications of AP. HTG may play a dominant role of risk in the condition of comorbidity. Chinese clinical trial registry ChiCTR2100049566. Registered on 3rd August, 2021. Retrospectively registered, https://www.chictr.org.cn/edit.aspx?pid=127374&htm=4.

Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models. Background As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Design Systematic review. Methods Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Results Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation. Conclusions ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. Relevance to Clinical Practice This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical practice.

ObjectiveThis study aimed to better understand the psychological experiences of inpatients with acute pancreatitis (AP).DesignWe used a qualitative descriptive study design to capture patients with AP’s thoughts, feelings and behavioural responses.SettingWe conducted this study in the gastroenterology departments of two tertiary hospitals in Eastern China.ParticipantsWe used a convenience sampling approach to recruit 28 inpatients with AP from 1 August 2020 to 25 December 2020. The interviews were audio-recorded and transcribed verbatim. We employed an adapted version of Colaizzi’s qualitative analysis approach to examine the data.ResultsWe extracted three themes and eight subthemes regarding the participants’ psychological experiences: (1) feeling that their disease is unpredictable (the inability to recognise the disease, uncertainty about the illness and fear of progression or recurrence); (2) various kinds of stress and support (feeling different degrees of stress, perceiving social support, seeking and craving social support); and (3) developing self-adaptability in the disease process (treating one’s illness negatively or positively).ConclusionsCognitive and emotional responses vary in patients with AP during hospitalisation. Moreover, patients with distinct conditions demonstrate significant differences in their responses and coping mechanisms. Healthcare providers need to mobilise social support and formulate comprehensive intervention strategies according to patients’ individual characteristics.