Development a clinical prediction model of the neurological outcome for patients with coma and survived 24 hours after cardiopulmonary resuscitation

Wang, Hai; Tang, Long; Zhang, Li; Zhang, Zheng Liang; Pei, Hong

doi:10.1002/clc.23403

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…7,13,19 In those patients with underlying cardiac disease as a contributing factor to CA, survival seems more likely. 7,20 There is a lack of large-scale population data on the impact of CA after SAH on mortality, and the discharge dispositions of those who do survive. To date, our understanding of this clinical syndrome is primarily based on smaller cohort and database studies.…”

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confidence: 99%

Cardiac arrest in spontaneous subarachnoid hemorrhage and associated outcomes

Feldstein

Dominguez

Kaur

et al. 2022

Neurosurgical Focus

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OBJECTIVE The authors sought to analyze a large, publicly available, nationwide hospital database to further elucidate the impact of cardiopulmonary arrest (CA) in association with subarachnoid hemorrhage (SAH) on short-term outcomes of mortality and discharge disposition. METHODS This retrospective cohort study was conducted by analyzing de-identified data from the National (Nationwide) Inpatient Sample (NIS). The publicly available NIS database represents a 20% stratified sample of all discharges and is powered to estimate 95% of all inpatient care delivered across hospitals in the US. A total of 170,869 patients were identified as having been hospitalized due to nontraumatic SAH from 2008 to 2014. RESULTS A total of 5415 patients (3.2%) were hospitalized with an admission diagnosis of CA in association with SAH. Independent risk factors for CA included a higher Charlson Comorbidity Index score, hospitalization in a small or nonteaching hospital, and a Medicaid or self-pay payor status. Compared with patients with SAH and not CA, patients with CA-SAH had a higher mean NIS Subarachnoid Severity Score (SSS) ± SD (1.67 ± 0.03 vs 1.13 ± 0.01, p < 0.0001) and a vastly higher mortality rate (82.1% vs 18.4%, p < 0.0001). In a multivariable model, age, NIS-SSS, and CA all remained significant independent predictors of mortality. Approximately 18% of patients with CA-SAH survived and were discharged to a rehabilitation facility or home with health services, outcomes that were most predicted by chronic disease processes and large teaching hospital status. CONCLUSIONS In the largest study of its kind, CA at onset was found to complicate roughly 3% of spontaneous SAH cases and was associated with extremely high mortality. Despite this, survival can still be expected in approximately 18% of patients.

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confidence: 99%

Cardiac arrest in spontaneous subarachnoid hemorrhage and associated outcomes

Feldstein

Dominguez

Kaur

et al. 2022

Neurosurgical Focus

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“…The points in the nomogram represent single scores corresponding to different values. The total points can be obtained by summing up the individual points [ 26 ]. Patients were divided into high-risk groups and low-risk groups according to the cutoff value.…”

Section: Discussionmentioning

confidence: 99%

A nomogram predicts cardiovascular events in patients with peritoneal dialysis-associated peritonitis

Huang

et al. 2022

Renal Failure

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Objective To predict the risk factors for cardiovascular events within 5 years in patients with peritoneal dialysis-associated peritonitis and establish a nomogram for clinical prediction. Methods A prediction model was established by conducting an observational study in 150 patients with peritoneal dialysis-associated peritonitis obtained from the Information Database of AnHui Medical University Affiliated Hospital. The nomogram was constructed using the multivariate COX regression model. The C-index and the calibration plot were used to assess the discrimination and calibration of the prediction model. Results The elderly [HR = 2.453 (1.071–5.619)], history of cardiovascular events [HR = 2.296 (1.220–4.321)], alkaline phosphatase [HR = 1.004 (1.002–1.005)] and culture-positive [HR= 2.173 (1.009–4.682)] were identified as risk predictors of cardiovascular events, while serum albumin [HR = 0.396(0.170–0.924)] was identified as protective predictors of cardiovascular events. Combined with clinical studies, we constructed a nomogram based on the minimum value of the Akaike Information Criterion or Bayesian Information Criterion. The C index of the nomogram is 0.732, revealing great discrimination and appropriate calibration. Through the total score of the nomogram and the result of ROC, we classify patients into high-risk groups (cardiovascular events group) and low-risk groups (no cardiovascular events group). Cardiovascular events were significantly different for patients in the high-risk group compared to the low-risk group (HR = 3.862(2.202–6.772; p < 0.001). Conclusions The current novel nomogram can accurately predict cardiovascular events in patients with peritonitis associated with peritoneal dialysis. However, external validation is required before the model can be used in clinic settings.

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“…The point in the figure represents a single score, and their single scores are added to obtain total points. 33 , 34 …”

Section: Nomogram Applicationmentioning

confidence: 99%

Nomogram Models for Predicting Delirium of Patients in Emergency Intensive Care Unit: A Retrospective Cohort Study

Yu¹,

Wang²,

Zhang³

et al. 2022

IJGM

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Background Intensive care unit (ICU) delirium is one of the most common clinical syndromes that results in many adverse events that affect patients, families, and hospitals. To date, there has been no tool for effectively predicting the occurrence of delirium in emergency intensive care unit (EICU) patients. Methods We conducted a retrospective cohort study and constructed a prediction model for 319 patients in EICU, who met our inclusion criteria. We analyzed the relationship between patients’ clinical data within 24 hours of admission and delirium, applied univariate and multivariate logistic regression analyses to select the most relevant variables for construction of nomogram models, then applied bootstrapping for internal validation. Results A total of five variables, namely stomach and urinary tubes, as well as sedative, mechanical ventilation and APACHE-II scores, were selected for model construction. We generated a total of five sets of models (three sets of construction models and two sets of internal verification models), with similar predictive value. The optimal model was selected, and together with the 5 variables used to construct a nomogram. The AUC of the MFP model in all patients was 0.76 (0.70, 0.82), whereas that in non-elderly patients (<60 years old) for the full model was 0.83 (0.74, 0.91). In elderly patients (≥60 years old), the AUC of the MFP model was 0.82 (0.73, 0.91). Conclusion Overall, the five-marker-based prognostic tool, established herein, can effectively predict the occurrence of delirium in EICU patients.

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Development a clinical prediction model of the neurological outcome for patients with coma and survived 24 hours after cardiopulmonary resuscitation

Cited by 7 publications

References 37 publications

Cardiac arrest in spontaneous subarachnoid hemorrhage and associated outcomes

Cardiac arrest in spontaneous subarachnoid hemorrhage and associated outcomes

A nomogram predicts cardiovascular events in patients with peritoneal dialysis-associated peritonitis

Nomogram Models for Predicting Delirium of Patients in Emergency Intensive Care Unit: A Retrospective Cohort Study

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