Dataset Information

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning.

ABSTRACT:

Objectives

To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival.

Methods

We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated using Area Under the Receiver Operating Characteristic curve (AUROC) and features were analyzed using SHapley Additive exPansion (SHAP) values.

Results

Baseline results using CARES data achieved an AUROC of 84%. The final model utilizing community variables increased the AUROC to 88%. A SHAP analysis between high and low performing community area clusters showed the high performing cluster is positively impacted by good health related features and good community safety features positively impact the low performing cluster.

Conclusion

Utilizing community variables helps predict neurologic outcomes with better performance than only CARES data. Future studies will use this model to perform simulations to identify interventions to improve OHCA survival.

SUBMITTER: Harford S

PROVIDER: S-EPMC9728593 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning.

Harford Sam S Darabi Houshang H Heinert Sara S Weber Joseph J Campbell Teri T Kotini-Shah Pavitra P Markul Eddie E Tataris Katie K Vanden Hoek Terry T Del Rios Marina M

Resuscitation 20220708

<h4>Objectives</h4>To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival.<h4>Methods</h4>We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated u ...[more]

PMID: 35817268

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Project description:Background: Early prognostication is a major challenge after out-of-hospital cardiac arrest (OHCA). Aims: We hypothesized that a genome-wide analysis of blood gene expression could offer new prognostic tools and lines of research. Methods: Sixty-nine patients were enrolled from an ancillary study of the clinical trial NCT00999583 that tested the effect of erythropoietin (EPO) after OHCA. Blood samples were collected in comatose survivors of OHCA at hospital admission and 1 and 3 days after resuscitation. Gene expression profiles were analyzed (Illumina HumanHT-12 V4 BeadChip; >34,000 genes). Patients were classified into two categories representing neurologically favorable outcome (Cerebral Performance Category [CPC] = 1-2) vs unfavorable outcome (CPC>2) at Day 60 after OHCA. Differential and functional enrichment analyses were performed to compare transcriptomic profiles between these two categories. Results: Among the 69 enrolled patients, 33 and 36 patients were treated or not by EPO, respectively. Among them, 42% had a favorable neurological outcome in both groups. EPO did not affect the transcriptomic response at Day-0 and 1 after OHCA. In contrast, 76 transcripts differed at Day-0 between patients with unfavorable vs favorable neurological outcome. This signature persisted at Day-1 after OHCA. Functional enrichment analysis revealed a down-regulation of adaptive immunity with concomitant up-regulation of innate immunity and inflammation in patients with unfavorable vs favorable neurological outcome. The transcription of many genes of the HLA family was decreased in patients with unfavorable vs favorable neurological outcome. Concomitantly, neutrophil activation and inflammation were observed. Up-stream regulators analysis showed the implication of numerous factors involved in cell cycle and damages. A logistic regression including a set of genes allowed a reliable prediction of the clinical outcomes (Specificity=88%; Hit Rate=83%). Conclusions: A transcriptomic signature involving a counterbalance between adaptive and innate immune responses is able to predict neurological outcome very early after hospital admission after OHCA. This deserves confirmation in a larger population.

Project description:To analyze functional performance measures collected prospectively during the conduct of a clinical trial that enrolled children (up to age 18 yr old), resuscitated after out-of-hospital cardiac arrest, who were at high risk of poor outcomes.Children with Glasgow Motor Scale score less than 5, within 6 hours of resuscitation, were enrolled in a clinical trial that compared two targeted temperature management interventions (THAPCA-OH, NCT00878644). The primary outcome, 12-month survival with Vineland Adaptive Behavior Scale, second edition, score greater or equal to 70, did not differ between groups.Thirty-eight North American PICUs.Two hundred ninety-five children were enrolled; 270 of 295 had baseline Vineland Adaptive Behavior Scale, second edition, scores greater or equal to 70; 87 of 270 survived 1 year.Targeted temperatures were 33.0°C and 36.8°C for hypothermia and normothermia groups.Baseline measures included Vineland Adaptive Behavior Scale, second edition, Pediatric Cerebral Performance Category, and Pediatric Overall Performance Category. Pediatric Cerebral Performance Category and Pediatric Overall Performance Category were rescored at hospital discharges; all three were scored at 3 and 12 months. In survivors with baseline Vineland Adaptive Behavior Scale, second edition scores greater or equal to 70, we evaluated relationships of hospital discharge Pediatric Cerebral Performance Category with 3- and 12-month scores and between 3- and 12-month Vineland Adaptive Behavior Scale, second edition, scores. Hospital discharge Pediatric Cerebral Performance Category scores strongly predicted 3- and 12-month Pediatric Cerebral Performance Category (r = 0.82 and 0.79; p < 0.0001) and Vineland Adaptive Behavior Scale, second edition, scores (r = -0.81 and -0.77; p < 0.0001). Three-month Vineland Adaptive Behavior Scale, second edition, scores strongly predicted 12-month performance (r = 0.95; p < 0.0001). Hypothermia treatment did not alter these relationships.In comatose children, with Glasgow Motor Scale score less than 5 in the initial hours after out-of-hospital cardiac arrest resuscitation, function scores at hospital discharge and at 3 months predicted 12-month performance well in the majority of survivors.

Project description:We aimed to determine the characteristics of quantitative pupillary response parameters other than amplitude of pupillary light reflex (PLR) early after return of spontaneous circulation (ROSC) and their implications for predicting neurological outcomes early after cardiac arrest (CA). Fifty adults resuscitated after non-traumatic out-of-hospital CA from four emergency hospitals were enrolled. Pupil diameters, PLR, constriction velocity (CV), maximum CV (MCV), dilation velocity (DV), latency of constriction, and Neurological Pupil index (NPi) were quantitatively measured at 0, 6, 12, 24, 48, and 72 h post-ROSC using an automated pupillometer. Change over time of each parameter was compared between favorable (Cerebral Performance Category [CPC] 1 or 2) and unfavorable neurological outcome (CPC 3-5) groups. Prognostic values of 90-day favorable outcome by these parameters and when combined with clinical predictors (witness status, bystander cardiopulmonary resuscitation, initial shockable rhythm, implementation of target temperature management) were tested. Thirteen patients achieved favorable outcome. CV, MCV, DV (P < 0.001), and NPi (P = 0.005) were consistently greater in the favorable group than in the unfavorable outcome group. Change over time was not statistically different between the groups in all parameters. CV, MCV, DV (ρ = 0.96 to 0.97, P < 0.001, respectively), and NPi (ρ = 0.65, P < 0.001) positively correlated with PLR. The prognostic value of 0-hour CV (area under the curve, AUC [95% confidence interval]: 0.92 [0.80-1.00]), DV (0.84 [0.68-0.99]), and NPi (0.88 [0.74-1.00]) was equivalent to that of PLR (0.84 [0.69-0.98]). Prognostic values improved to AUC of 0.95-0.96 when 0-hour PLR, CV, DV, or NPi was combined with clinical predictors. The 0-hour CV, MCV, and NPi showed equivalent prognostic values to PLR alone/in combination with clinical predictors. Using PLR among several quantitative pupillary response parameters for early neurological prognostication of post-CA patients is a simple and effective strategy.

Dataset Information

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning.

Objectives

Methods

Results

Conclusion

Publications

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning.

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