Marcin J P, Slonim A D, Pollack M M, Ruttimann U E
Section of Critical Care Medicine, Department of Pediatrics, University of California, Davis, Sacramento, CA, USA.
Crit Care Med. 2001 Mar;29(3):652-7. doi: 10.1097/00003246-200103000-00035.
Length of stay in the pediatric intensive care unit (PICU) is a reflection of patient severity of illness and health status, as well as PICU quality and performance. We determined the clinical profiles and relative resource use of long-stay patients (LSPs) and developed a prediction model to identify LSPs for early quality and cost saving interventions.
Nonconcurrent cohort study.
A total of 16 randomly selected PICUs and 16 volunteer PICUs.
A total of 11,165 consecutive admissions to the 32 PICUs.
None.
LSPs were defined as patients having a length of stay greater than the 95th percentile (>12 days). Logistic regression analysis was used to determine which clinical characteristics, available within the first 24 hrs after admission, were associated with LSPs and to create a predictive algorithm. Overall, LSPs were 4.7% of the population but represented 36.1% of the days of care. Multivariate analysis indicated that the following factors are predictive of long stays: age <12 months, previous ICU admission, emergency admission, no CPR before admission, admission from another ICU or intermediate care unit, chronic care requirements (total parenteral nutrition and tracheostomy), specific diagnoses including acquired cardiac disease, pneumonia, and other respiratory disorders, having never been discharged from the hospital, need for ventilatory support or an intracranial catheter, and a Pediatric Risk of Mortality III score between 10 and 33. The performance of the prediction algorithm in both the training and validation samples for identifying LSPs was good for both discrimination (area under the receiver operating characteristics curve of 0.83 and 0.85, respectively), and calibration (goodness of fit, p = .33 and p = .16, respectively). LSPs comprised from 2.1% to 8.1% of individual ICU patients and occupied from 15.2% to 57.8% of individual ICU bed days.
LSPs have less favorable outcomes and use more resources than non-LSPs. The clinical profile of LSPs includes those who are younger and those that require chronic care devices. A predictive algorithm could help identify patients at high risk of prolonged stays appropriate for specific interventions.
小儿重症监护病房（PICU）的住院时间反映了患者的疾病严重程度和健康状况，以及PICU的质量和绩效。我们确定了长期住院患者（LSP）的临床特征和相对资源使用情况，并开发了一种预测模型，以识别LSP，以便早期进行质量和成本节约干预。
非同期队列研究。
共16个随机选择的PICU和16个志愿PICU。
32个PICU共有11165例连续入院患者。
无。
LSP定义为住院时间大于第95百分位数（>12天）的患者。采用逻辑回归分析来确定入院后最初24小时内可用的哪些临床特征与LSP相关，并创建一个预测算法。总体而言，LSP占总人数的4.7%，但占护理天数的36.1%。多变量分析表明，以下因素可预测长期住院：年龄<12个月、既往入住ICU、急诊入院、入院前未进行心肺复苏、从另一个ICU或中级护理单元转入、慢性护理需求（全胃肠外营养和气管切开术）、特定诊断，包括获得性心脏病、肺炎和其他呼吸系统疾病、从未出院、需要通气支持或颅内导管，以及小儿死亡风险III评分在10至33之间。预测算法在训练样本和验证样本中识别LSP的性能在区分度（受试者操作特征曲线下面积分别为0.83和0.85）和校准度（拟合优度，p值分别为0.33和0.16）方面均良好。LSP占各ICU患者的2.1%至8.
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