A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning

Development and validation of a prognostic nomogram for predicting of patients with acute sedative-hypnotic overdose admitted to the intensive care unit

To develop and evaluate a predictive model for intensive care unit (ICU) admission among patients with acute sedative-hypnotic overdose. We conducted a retrospective analysis of patients admitted to the emergency department of West China Hospital, Sichuan University, between October 11, 2009, and December 31, 2023. Patients were divided into ICU and non-ICU groups based on admission criteria including the need for blood purification therapy, organ support therapy (ventilatory support, vasoactive drugs, renal replacement therapy, artificial liver), or post-cardiopulmonary resuscitation. Patients were randomly split into a training set and a validation set in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to optimize variables, followed by a multivariate logistic regression analysis to identify independent risk factors for ICU admission. A nomogram model was constructed and assessed using receiver operating characteristic (ROC) curves, calibration curves, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC). Predictors in the nomogram included barbiturate overdose, Glasgow Coma Scale (GCS) score, and anion gap at admission. The nomogram demonstrated strong predictive performance with an area under the curve (AUC) of 0.858 (95% CI: 0.788-0.927) in the training set and 0.845 (95% CI: 0.757-0.933) in the validation set. Calibration curves showed the model closely matched the ideal curve, and DCA and CIC indicated high clinical applicability and utility. Barbiturate overdose, initial decreased GCS score and decreased anion gap were identified as independent risk factors for ICU admission in acute sedative-hypnotic overdose. The nomogram model based on these indicators demonstrates good predictive accuracy, discrimination, and clinical utility.

Development and internal validation of a predictive model for prolonged intensive care unit stays in patients with psychotropic drug poisoning

BACKGROUND

Some patients with psychotropic drug poisoning need intensive care unit (ICU) admission, but risk prediction models for prolonged ICU stays are lacking.

OBJECTIVES

Develop and evaluate a prediction model for prolonged ICU stays in patients with psychotropic drug poisoning.

METHODS

The clinical data of patients with psychotropic drug poisoning were collected from the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ 2.2 database. Patients were grouped by their ICU length of stay: non-prolonged (<2 days) and prolonged (≥2 days). Variable selection methods included LASSO and logistic regression. The selected variables were used to construct the model, which was subsequently evaluated for discrimination, calibration, and clinical utility.

RESULTS

The cohort included 413 patients with psychotropic drug poisoning, 49.4 % male, with a median age of 41 years. The variables stepwise selected for model construction through LASSO and logistic regression include sepsis, SAPS Ⅱ, heart rate, respiratory rate, and mechanical ventilation. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.785 (95 % CI: 0.736-0.833) and was validated well with bootstrap internal validation (AUC: 0.792, 95 % CI: 0.745-0.839). Calibration curves indicated good fit (χ2 = 4.148, P = 0.844), aligning observed and predicted rates of prolonged ICU stays. Decision curve analysis (DCA) showed positive net benefits across a threshold probability range of 0.07-0.85.

CONCLUSIONS

The model developed in this study may help predict the risk of prolonged ICU stays for patients with psychotropic drug poisoning.

Development of a risk prediction nomogram for delayed neuropsychiatric sequelae in patients with acute carbon monoxide poisoning

OBJECTIVES

Delayed neuropsychiatric sequelae (DNS) are critical complications following acute carbon monoxide (CO) poisoning that can substantially affect the patient's life. Identifying high-risk patients for developing DNS may improve the quality of follow-up care. To date, the predictive DNS determinants are still controversial. Consequently, this study aimed to construct a practical nomogram for predicting DNS in acute CO-poisoned patients.

METHODS

This retrospective study was conducted on patients with acute CO poisoning admitted to the Tanta University Poison Control Center (TUPCC) from December 2018 to December 2022. Demographic, toxicological, and initial clinical characteristics data, as well as laboratory investigation results, were recorded for the included patients. After acute recovery, patients were followed up for six months and categorized into patients with and without DNS.

RESULTS

Out of 174 enrolled patients, 38 (21.8%) developed DNS. The initial Glasgow Coma Scale (GCS), carboxyhemoglobin (COHb) level, CO exposure duration, oxygen saturation, PaCO2, and pulse rate were significantly associated with DNS development by univariate analysis. However, the constructed nomogram based on the multivariable regression analysis included three parameters: duration of CO exposure, COHb level, and GCS with adjusted odd ratios of 1.453 (95% CI: 1.116-1.892), 1.262 (95% CI: 1.126-1.415), and 0.619 (95% CI: 0.486-0.787), respectively. The internal validation of the nomogram exhibited excellent discrimination (area under the curve [AUC] = 0.962), good calibration, and satisfactory decision curve analysis for predicting the DNS probability.

CONCLUSIONS

The proposed nomogram could be considered a simple, precise, and applicable tool to predict DNS development in acute CO-poisoned patients.

Predictors for prolonged qt intervals in acute antipsychotic poisoned patients

Background

Acute antipsychotic poisoning is correlated to a high prevalence of qt interval prolongation.

Aim

This study aimed to evaluate early qt interval prolongation predictors in acute antipsychotic-poisoned patients.

Methodology

This prospective cohort study enrolled 70 symptomatic patients with acute antipsychotic poisoning. Sociodemographic data, toxicological, clinical, investigation, and outcomes were collected and analyzed. The estimation of the corrected qt interval (QTc) was performed using Bazett's method. Primary outcome was normal or abnormal length of QTc interval. Secondary outcomes included duration of hospital stay, complete recovery and mortality. The corrected qt interval was analyzed by univariate and multivariate logistic regression analysis.

Results

Patients were divided into groups A (normal QTc interval up to 440 msec; 58.6% of cases) and B (prolonged QTc interval ≥ 440 msec; 41.4% of cases). Patients in group B had significantly high incidences of quetiapine intake, bradycardia, hypotension, hypokalemia, and long duration of hospital stay. By multivariate analysis, quetiapine [Odd's ratio (OR): 39.674; Confidence Interval (C.I:3.426-459.476)], bradycardia [OR: 22.664; C.I (2.534-202.690)], and hypotension [OR: 16.263; (C.I: 2.168-122.009)] were significantly correlated with prolonged QTc interval.

Conclusion

In acute antipsychotic poisoning, quetiapine, bradycardia, and hypotension are early clinical predictors for prolonged QTc interval.

predictive model of nosocomial infection in patients with upper urinary tract stones after flexible ureterorenoscopy with laser lithotripsy: A retrospective study

Objectives

To construct a predictive model of nosocomial infection in patients with upper urinary tract (UUT) stones after flexible ureterorenoscopy with laser lithotripsy (FURSLL).

Methods

Medical records of 196 patients with UUT stones who underwent FURSLL in Suzhou Hospital of Integrated Traditional Chinese and Western Medicine from December 2019 to December 2022 were retrospectively analyzed. Patients were divided into infected group or uninfected group based on the presence of infection during postoperative hospitalization. Univariate and multivariate logistic regressions were used to identify risk factors of postoperative nosocomial infections. A nomogram prediction model was constructed using R software. The predictive ability of the model was assessed using the receiver operating characteristic (ROC) curve.

Results

A total of 54 patients (27.6%) developed nosocomial infections after FURSLL. Logistic regression analysis showed that older age, diabetes, preoperative urinary system infection, ureteral stricture, hydronephrosis, double J-stent retention time, and stone diameter were risk factors of nosocomial infection. The nomogram model was constructed based on these risk factors. The ROC showed that the area under the curve (AUC) of the model was 0.930 (95% CI: 0.890-0.970), and the sensitivity and specificity were 92.6% and 81.7%, respectively, indicating that the prediction model was effective.

Conclusions

Risk of nosocomial infection in patients with UUT stones after FURSLL is affected by older age, diabetes, preoperative urinary system infection, ureteral stenosis, hydronephrosis, double J-stent retention time, and stone diameter. The nomogram prediction model, constructed based on the above factors, has good predictive value.

Development and validation of a nomogram for predicting mechanical ventilation need among acutely intoxicated patients with impaired consciousness

BACKGROUND

A considerable portion of acutely intoxicated patients is presented with impaired consciousness. Early identification of those patients who require advanced medical care, such as mechanical ventilation (MV), can improve their prognosis.

METHODS

This study included 330 acutely intoxicated patients who were presented with impaired consciousness and admitted to Tanta University Poison Control Center, Egypt, in the period from January 2021 to December 2023. Patients were enrolled in derivation (257 patients) and validation (73 patients) cohorts. Patients' data were analyzed to develop and validate a predictive nomogram to determine the probability of MV need in acutely intoxicated patients.

RESULTS

Significant predictors for MV need were mean arterial blood pressure (OR = 0.96, p = .014), PaO2 (OR = 0.96, p = .001), pH (OR = 0.00, p < . 001), and glucose/potassium ratio (OR = 1.59, p = .030). These four parameters were used to formulate a bedside nomogram. Receiver-operating characteristic (ROC) analysis for the proposed nomogram shows that area under the curve (AUC) = 95.7%, accuracy = 93.4%, sensitivity = 88.9%, and specificity = 95.1%. The internal validation for the developed nomogram was assessed using a bootstrapping method and calibration curve. Regarding external validation, AUCs for the developed nomogram probability was 96.5%, and for predicted probability using the developed nomogram was 97.8%.

CONCLUSION

The current study provides a validated nomogram that could be used as a reliable tool for the accurate prediction of MV need among acutely intoxicated patients with impaired consciousness. It could assist in the early identification of patients who will require MV, especially in low-income countries with limited resources.