SALAD-BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease.
SALAD-BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease.
复制标题
DOI：
10.1002/clc.23525
复制
发表时间：
2021-03
影响因子：
2.7
通讯作者：
Tabit CE
中科院分区：
文献类型：
--
作者：
Anyanwu EC;Chua RFM;Besser SA;Sun D;Liao JK;Tabit CE
虽然已经开发了许多减少心血管疾病患者住院和急诊（ED）就诊的干预措施，但确定住院高风险的门诊心脏病患者可能具有挑战性。基于易于获取的临床数据的计算模型可以识别有入院风险的患者。来自三级转诊中心的电子健康记录（EHR）数据用于生成决策树和逻辑回归模型。使用国际疾病分类（ICD）代码、实验室、入院情况、药物、生命体征和社会环境变量来模拟心脏病门诊就诊后90天内ED表现或住院的风险。模型训练和测试以70:30的数据分割进行。然后对最终模型进行前瞻性验证。共分析9326例患者和46 465次就诊。使用75个患者特征的决策树模型的曲线下面积（AUC）为0.75，逻辑回归模型的AUC为0.73。基于逻辑回归优势比的简化9个特征模型的AUC为0.72。进一步简化数值评分，每个变量赋1或2分，AUC为0.66，特异性为0.75，敏感性为0.58。最终模型保持了预测性能（AUC 0.63-0.60）。来自常规EHR数据的9个患者特征可用于告知心脏病患者住院或ED表现的高度特异性模型。该模型可以简化为易于计算并保留预测性能的风险评分。
While many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging. A computational model based on readily accessible clinical data can identify patients at risk for admission. Electronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated. A total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60). Nine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.
登录
查看更多内容
影响因子：
2.7
作者：
Anyanwu EC;Chua RFM;Besser SA;Sun D;Liao JK;Tabit CE
通讯作者：
Tabit CE
影响因子：
4.8
作者：
LEIER, CV;CAS, LD;METRA, M
通讯作者：
METRA, M
影响因子：
37.8
作者：
Klein, L;O'Connor, CM;Gheorghiade, M
通讯作者：
Gheorghiade, M
影响因子：
39
作者：
Donze, Jacques;Aujesky, Drahomir;Schnipper, Jeffrey L.
通讯作者：
Schnipper, Jeffrey L.
影响因子：
37.8
作者：
Benjamin EJ;Blaha MJ;Chiuve SE;Cushman M;Das SR;Deo R;de Ferranti SD;Floyd J;Fornage M;Gillespie C;Isasi CR;Jiménez MC;Jordan LC;Judd SE;Lackland D;Lichtman JH;Lisabeth L;Liu S;Longenecker CT;Mackey RH;Matsushita K;Mozaffarian D;Mussolino ME;Nasir K;Neumar RW;Palaniappan L;Pandey DK;Thiagarajan RR;Reeves MJ;Ritchey M;Rodriguez CJ;Roth GA;Rosamond WD;Sasson C;Towfighi A;Tsao CW;Turner MB;Virani SS;Voeks JH;Willey JZ;Wilkins JT;Wu JH;Alger HM;Wong SS;Muntner P;American Heart Association Statistics Committee and Stroke Statistics Subcommittee
通讯作者：
American Heart Association Statistics Committee and Stroke Statistics Subcommittee