Daily Ards Research Analysis

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers. This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. METHODS: This prospective cohort study in China was conducted on critically ill patients to derivate and validate the prediction model. The derivation cohort, consisting of 400 patients admitted to the ICU of the Peking University Third Hospital(PUTH) between December 2020 and August 2023, was separated for training and internal validation, and an external data set of 160 patients at the FU YANG People's Hospital from August 2022 to August 2023 was employed for external validation. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen predictor variables. Multiple ML classification models were integrated to analyze and identify the best models. Several evaluation indexes were used to compare the model performance, including the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). SHapley Additive ex Planations (SHAP) is used to interpret ML models. RESULTS: 400 critically ill patients were included in the analysis, with 117 developing ARDS during follow-up. The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.762-0.910). For external validation, comprising 160 patients, 44 of whom developed ARDS, the AUC was 0.799 (95% CI: 0.723-0.875), significantly outperforming the LIPS score alone. CONCLUSION: Combining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

OBJECTIVES: The objective of this study was to identify factors associated with in-hospital deaths of newborns admitted to a special care newborn unit (SCANU) in southern Bangladesh. DESIGN: Retrospective cohort. SETTING: SCANU of Patuakhali Medical College Hospital, Patuakhali, Bangladesh. PARTICIPANTS: Records of 930 neonates admitted to the SCANU from August to December 2022 were included in the study. The inclusion criteria consisted of neonates admitted during the specified period, while the exclusion criteria excluded records of newborns who were referred to intensive care units or who left against medical advice. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: In-hospital neonatal mortality. SECONDARY OUTCOMES: Specific conditions or factors affecting the in-hospital deaths. RESULTS: Of the 930 neonates analysed, 3.44% died in the hospital. Factors significantly associated with shorter survival time and increased in-hospital mortality included low birth weight (p=0.004), preterm delivery (p=0.022) and respiratory distress syndrome (RDS) (p=0.002). RDS showed an independent association with death in the hospital (adjusted HR: 3.39; 95% CI: 1.11 to 10.35). Newborns delivered at home or in an ambulance had a 2.90 times higher hazard of dying in the hospital (95% CI: 1.17 to 7.17) compared with those delivered at the hospital. CONCLUSIONS: Addressing preterm birth, low birth weight and respiratory distress, along with promoting institutional deliveries, is crucial for reducing neonatal mortality rates in resource-limited settings like Bangladesh.

The clinical profile and outcomes of children with chikungunya infection differ from those observed in adults. As there is a paucity of data on chikungunya infection in children, this study aimed to find the clinical course, complications, and mortality rates of chikungunya infection in children. This was a combined retrospective and prospective observational study. Children aged 1 month to 15 years who tested positive for chikungunya infection by IgM enzyme-linked immunosorbent assay and reverse transcription polymerase chain reaction in serum or body fluids were included. The demographic details, clinical presentation, laboratory parameters, treatment given, and outcomes were recorded in a structured proforma. Fifty-eight cases (41 retrospective and 17 prospective) were recruited, out of which 30 (52%) were males. The median age was 8 (3-11) years. The most common clinical feature at admission was fever observed in 55 patients (94.8%), followed by vomiting [25 (43.1%)] and myalgia [23 (39.7%)]. Commonly observed clinical signs were skin rash [32 (55.2%)], hepatomegaly [25 (43.1%)], and anemia [22 (37.9%)]. Frequently observed acute complications were lymphopenia [46 (79.3%)], hyponatremia [32 (55.2%)], capillary leak [27 (46.6%)], and thrombocytopenia [26 (44.8%)]. Of 58 cases, 8 (13.8%) children had co-infection with other microbes. Overall, 55 (94.8%) children had complete recovery, 2 (3.4%) children died of complications (one with acute encephalitis and one child with acute respiratory distress syndrome), and 5 children had prolonged arthralgia. Children with chikungunya had more skin manifestations and neurological manifestations than arthralgia. Also, a significant proportion of children developed serious complications like a capillary leak.