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Evaluation of the E-PRE-DELIRIC prediction model for ICU delirium: a retrospective validation in a UK general ICU
Critical Care ( IF 8.8 ) Pub Date : 2020-03-30 , DOI: 10.1186/s13054-020-2838-2
Sarah L. Cowan , Jacobus Preller , Robert J. B. Goudie

Methods We retrospectively analysed data for 2445 consecutive ICU admissions (November 2014 to June 2017). Patients were routinely assessed for delirium, using twice daily Confusion Assessment Method for the ICU (CAM-ICU) assessment [5]. As in previous E-PRE-DELIRIC studies [1–4], delirium was defined as any positive CAM-ICU assessment or antipsychotic initiation while on ICU. We adopted the original E-PRE-DELIRIC exclusion criteria [1], excluding 683 ICU admissions for ICU stay < 24 h (425 admissions), incomplete CAM-ICU data (152), delirium on admission (50), comatose throughout entire ICU stay (47), and age under 18 (9). Sixteen admissions were excluded due to missing E-PRE-DELIRIC components; 1746 admissions (1569 unique patients) remained for analysis; this 71.4% inclusion rate is consistent with previous studies (Table 1). Results and discussion Seven hundred sixty-three delirium cases were identified (43.7% of ICU admissions), a higher incidence than reported previously (Table 1). This is likely due to differences in the study population compared to previous studies: more patients were classified as urgent, the mean APACHE II score was higher, and median length of stay (LoS) was longer (Table 1). The mean E-PRE-DELIRIC score was 0.269 (Q1–Q3; 0.154–0.371). The histogram of E-PRE-DELIRIC scores shows extensive overlap between patients who did and did not develop delirium (Fig. 1a). The receiver operator characteristic (ROC) curve (Fig. 1b) and the precisionrecall (PR) curve (Fig. 1c), showing precision (positive predictive value (PPV)) against recall (sensitivity), both indicate moderate-to-poor discriminative performance. The area under the ROC (AUROC) was 0.628 (95% CI 0.602–0.653). The area under the PR curve (AUPRC) was 0.534. For sensitivity > 0.1, PPV was between 0.437 and 0.585, indicating only around half of the patients predicted to develop delirium actually did, in a population with 43.7% incidence. Refitting the E-PRE-DELIRIC logistic regression model to our data hardly improved discrimination: AUROC was 0.648 (95% CI 0.622–0.673) and AUPRC was 0.566. The calibration plot, of predicted risk against observed delirium rate, shows the risk of delirium is considerably underestimated, especially in patients with predicted risk of delirium less than 0.5 (Fig. 1d). Poor calibration is corroborated by the calibration slope model logit(probability of delirium) = alpha + beta ×logit(p), where p is

中文翻译:

ICU 谵妄的 E-PRE-DELIRIC 预测模型评估:在英国普通 ICU 的回顾性验证

方法 我们回顾性分析了 2445 名连续入住 ICU 的数据(2014 年 11 月至 2017 年 6 月)。使用每天两次的 ICU 混乱评估方法 (CAM-ICU) 对患者进行常规评估谵妄 [5]。与之前的 E-PRE-DELIRIC 研究 [1-4] 一样,谵妄被定义为任何阳性 CAM-ICU 评估或在 ICU 期间开始使用抗精神病药。我们采用了原始的 E-PRE-DELIRIC 排除标准 [1],排除了 683 名 ICU 入住时间 < 24 小时(425 人)、不完整的 CAM-ICU 数据(152 人)、入院时谵妄(50 人)、整个 ICU 昏迷逗留 (47) 和 18 岁以下 (9)。由于缺少 E-PRE-DELIRIC 组件,16 名入院患者被排除在外;1746 名住院患者(1569 名独特患者)仍待分析;这 71.4% 的纳入率与之前的研究一致(表 1)。结果和讨论 确定了 763 例谵妄病例(占 ICU 入院人数的 43.7%),发生率高于之前报告的(表 1)。这可能是由于研究人群与之前的研究相比存在差异:更多的患者被归类为急诊,平均 APACHE II 评分更高,中位住院时间 (LoS) 更长(表 1)。平均 E-PRE-DELIRIC 得分为 0.269(Q1-Q3;0.154-0.371)。E-PRE-DELIRIC 评分的直方图显示发生和未发生谵妄的患者之间存在广泛重叠(图 1a)。接受者操作特征(ROC)曲线(图 1b)和精确召回(PR)曲线(图 1c),显示精确度(阳性预测值(PPV))对召回(敏感性),都表明中等至差的辨别力表现。中华民国 (AUROC) 下的面积为 0。628 (95% CI 0.602–0.653)。PR 曲线下面积 (AUPRC) 为 0.534。对于 > 0.1 的敏感性,PPV 介于 0.437 和 0.585 之间,这表明在一个发生率 43.7% 的人群中,只有大约一半预计会发生谵妄的患者实际上会发生。将 E-PRE-DELIRIC 逻辑回归模型重新拟合到我们的数据几乎没有改善区分:AUROC 为 0.648(95% CI 0.622–0.673),AUPRC 为 0.566。预测风险与观察到的谵妄率的校准图显示谵妄的风险被大大低估,尤其是在谵妄预测风险小于 0.5 的患者中(图 1d)。校准斜率模型 logit(谵妄概率) = alpha + beta ×logit(p) 证实了校准不佳,其中 p 是 PPV 介于 0.437 和 0.585 之间,表明在发生率 43.7% 的人群中,只有大约一半预计会出现谵妄的患者实际上会发生。将 E-PRE-DELIRIC 逻辑回归模型重新拟合到我们的数据几乎没有改善区分:AUROC 为 0.648(95% CI 0.622–0.673),AUPRC 为 0.566。预测风险与观察到的谵妄率的校准图显示谵妄的风险被大大低估,尤其是在谵妄预测风险小于 0.5 的患者中(图 1d)。校准斜率模型 logit(谵妄概率) = alpha + beta ×logit(p) 证实了校准不佳,其中 p 是 PPV 介于 0.437 和 0.585 之间,表明在发生率 43.7% 的人群中,只有大约一半预计会出现谵妄的患者实际上会发生。将 E-PRE-DELIRIC 逻辑回归模型重新拟合到我们的数据几乎没有改善区分:AUROC 为 0.648(95% CI 0.622–0.673),AUPRC 为 0.566。预测风险与观察到的谵妄率的校准图显示谵妄的风险被大大低估,尤其是在谵妄预测风险小于 0.5 的患者中(图 1d)。校准斜率模型 logit(谵妄概率) = alpha + beta ×logit(p) 证实了校准不佳,其中 p 是 将 E-PRE-DELIRIC 逻辑回归模型重新拟合到我们的数据几乎没有改善区分:AUROC 为 0.648(95% CI 0.622–0.673),AUPRC 为 0.566。预测风险与观察到的谵妄率的校准图显示谵妄的风险被大大低估,尤其是在谵妄预测风险小于 0.5 的患者中(图 1d)。校准斜率模型 logit(谵妄概率) = alpha + beta ×logit(p) 证实了校准不佳,其中 p 是 将 E-PRE-DELIRIC 逻辑回归模型重新拟合到我们的数据几乎没有改善区分:AUROC 为 0.648(95% CI 0.622–0.673),AUPRC 为 0.566。预测风险与观察到的谵妄率的校准图显示谵妄的风险被大大低估,尤其是在谵妄预测风险小于 0.5 的患者中(图 1d)。校准斜率模型 logit(谵妄概率) = alpha + beta ×logit(p) 证实了校准不佳,其中 p 是
更新日期:2020-03-30
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