Length of stay in pediatric intensive care unit

March 31, 2022

1.1 Scope of Review

The following review of the past work done in the area of intensive care unit (ICU) length of stay is divided into two parts. The first part covers the studies done on the PICU length of stay while the second part delves into the literature of ICU length of stay.

1.2 Studies of Length of Stay in Pediatric Intensive Care Unit

Ruttimann & Pollack (1996) investigated the relationship of length of pediatric intensive care unit (PICU) stay to severity of illness and other potentially relevant factors available within the first 24 hours after admission. A median and geometric mean length of PICU stay of 2.0 and 1.9 days respectively, and the upper 95th percentile at 12 days were found. To prevent undue influence of outliers, all patients staying longer than 12 days were considered long-stay patients (4.1% of the total sample) and were excluded from the model-building process. In the LOS prediction model, variables found to be significantly associated (p <0.05) with prolonged length of PICU stay included Pediatric Risk of Mortality (PRISM) score, 10 diagnostic groups, 3 preadmission factors (operative status, inpatient/outpatient, previous PICU admission), and first-day use of mechanical ventilation (Table 2.1).

Table 1.1: Log-logistic regression model for length of stay

Variable		Regression coefficient	SE	Adjusted LOS ratio	95% CI
PRISM score*		0.6386	0.0407
	5			1.28	1.25-1.33
	10			1.63	1.54-1.74
	15			1.80	1.67-1.94
	20			1.98	1.82-2.16
	25			1.62	1.53-1.72
	30			1.29	1.25-1.33
	40			1.38	1.33-1.44
	50			1.06	1.06-1.07
Primary diagnoses
	CNS diseases	-0.1682	0.0267	0.85	0.80-0.89
	Neoplastic diseases	0.2324	0.0579	1.26	1.13-1.41
	Drug overdoses	-0.1758	0.0383	0.84	0.77-0.90
	Inguinal hernia	-0.3270	0.1344	0.72	0.55-0.94
	Asthma	-0.1135	0.0527	0.89	0.80-0.99
	Pneumonia	0.2350	0.0475	1.26	1.15-1.39
	CNS infections	0.4966	0.0555	1.64	1.47-1.83
	Respiratory diseases Ã- PRISMâ€	0.1257	0.0579	1.67	1.49-1.87
	Head trauma Ã- PRISMâ€	0.1710	0.0611	1.73	1.53-1.94
	Diabetes Ã- PRISMâ€	-0.3332	0.0666	1.23	1.08-1.40
Admission conditions
	Postoperative	0.1267	0.0243	1.14	1.08-1.19
	Inpatient	0.2358	0.0271	1.27	1.20-1.33
	Previous ICU admission	0.1562	0.0521	1.17	1.06-1.29
Therapy
	Mechanical ventilation	0.4900	0.0258	1.63	1.55-1.72
Intercept		-0.0191	0.0278
Scale		2.5602	0.0295

Log partial likelihood = -5487.2; global chi-square value = 1601.9; df = 15; p <0.0001

CI, Confidence interval; CNS, Central nervous system

*LOS ratios computed relative to PRISM score = 0.

â€ LOS ratios computed for an interaction with PRISM score = 6.42 (sample average).

Source: Modified from Ruttimann & Pollack (1996).

In the same study, Ruttimann & Pollack (1996) noted the ratio of observed to predicted LOS varied among PICUs from 0.83 to 1.25. The PICU factors associated (p <0.05) with shorter (5% to 11%) LOS were presence of an intensivist, presence of residents, and coordination of care, whereas an increased ratio of PICU to hospital beds was associated with longer (p <0.05) LOS (Table 2.2). After adjusting to patient conditions, medical school affiliation, admission volume, number of pediatric hospital beds, and PICU mortality rates did not have statistically significant effect on LOS. The study proposed the use of predictors to adjust LOS in PICUs for patient-related risk factors at admission, and hence enabling the comparison of resource utilization among different institutions.

Table 1.2: Effect of PICU characteristics on length of stay

Variable	Regression coefficient	SE	Adjusted LOS ratio	95% CI	p*
Intensivist	-0.1208	0.0189	0.89	0.85-0.92	0.0001
Coordination	-0.0513	0.0190	0.95	0.92-0.99	0.0071
Residents	-0.0586	0.0200	0.94	0.91-0.98	0.0033
ln (PICU/hospital beds) â€	0.0459	0.0170	1.03	1.01-1.06	0.0068

CI, Confidence interval.

*2 Ã- ln (likelihood ratio) test.

â€ LOS ratio and 95% CIs computed for and increase of PICU/hospital bed ratio by a factor of 2.

Source: Modified from Ruttimann & Pollack (1996).

Development of a new LOS prediction model was necessary due to the availability of a newly updated pediatric severity-of-illness assessment system, PRISM III-24 (Pediatric risk of mortality, version III, 24-hour assessment). Ruttimann et al. (1998) have then fitted a generalized linear regression model (inverse Gaussian) to the observed LOS data with the log link function. In the new LOS prediction model, variables found to be significantly associated (p <0.05) with prolonged length of PICU stay included PRISM III-24 score, 8 diagnostic groups, 3 preadmission factors (operative status, inpatient/outpatient, previous PICU admission), and first-day use of mechanical ventilation (Table 2.3).

Table 1.3: Generalized linear regression model (inverse Gaussian) for length of stay (n = 9558)

Variable		Length of stay ratio	95% Confidence interval	p Valueâ€
PRISM III-24		â€¡	â€¡	0.0001
(PRISM III-24)°°2		â€¡	â€¡	0.0001
Primary diagnoses
	CNS infections	1.41	1.28-1.56	0.0001
	Neoplastic diseases	1.22	1.13-1.31	0.0001
	Asthma	0.91	0.85-0.96	0.0045
	Pneumonia	1.50	1.40-1.61	0.0001
	Drug overdoses	0.74	0.70-0.79	0.0001
	CV nonoperative	1.22	1.14-1.32	0.0001
	CV operative	0.89	0.83-0.95	0.0006
	Diabetes	0.74	0.67-0.81	0.0001
Admission specifications
	Postoperative	0.92	0.88-0.96	0.0004
	Inpatient	1.17	1.13-1.22	0.0001
	Previous ICU admission	1.26	1.15-1.38	0.0001
Therapy
	Mechanical ventilation	1.68	1.60-1.77	0.0001
Model intercept (± SEM) = 1.423 ± 0.021 days

CNS, Central nervous system; CV, cardiovascular system.

°Effect of the variable after adjusting for the effects of all other variables in the model.

â€ Log-likelihood ratio compared with the chi-squared distribution with 1 degree of freedom.

â€¡See Fig.2 (pg 82, Ruttimann et al. 1998).

Model fit: Scaled deviance = 9558 (chi-square with 9543 degrees of freedom, p >0.45). Observed versus predicted length of stay, mean (± SEM) in: training sample (n = 9,558): 2.351(± 0.032) versus 2.360(± 0.011), p >0.64; test sample (n = 1,100): 2.461(± 0.069) versus 2.419(± 0.035), p >0.49.

Source: Modified from Ruttimann et al. (1998).

Ruttimann et al. (1998) have also assessed the PICU efficiency with the new LOS prediction model and validation of the assessment by an efficiency measure based on daily use of intensive care unit-specific therapies (based on the criterion whether on each day a patient used at least one therapy that is best delivered in the ICU). PICU efficiency was computed as either the ratio of the observed efficient days or the days accounted for by the predictor variables to the total care days, and the agreement was assessed by Spearman’s rank correlation analysis. PICU efficiency comparisons for both the predictor-based and therapy-based methods are nearly equivalent. Ruttimann and colleagues (1998) acknowledged the advantage of predictor-based efficiency as it can be computed from admission day data only.

It was of researchers’ utmost interest to study the extended LOSs as well. Long-stay patients (LSPs) in the PICU were later being examined by Marcin et al. (2001). As explained previously, LSPs were defined as patients having a length of stay greater than 95th percentile (>12 days). In the study, the clinical profiles and relative resource use of LSPs were determined and a prediction model was developed to identify LSPs for early quality and cost saving interventions. To create a predictive algorithm, logistic regression analysis was used to determine clinical characteristics, available within the first 24 hours after admission that were associated with LSPs. Marcin and colleagues (2001) noted that, “Long-stay patients in the PICU consume a disproportionate amount of health care resources and have higher mortality rates than short-stay patients.”

Multivariate analysis of the study identified predictive factors of long-stay as: 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 (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 (PRISM III) score between 10 and 33 (Table 2.4). Marcin et al. (2001) concluded that 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. The predictive algorithm was expected to help in identifying patients at high risk of prolonged stays appropriate for specific interventions.

Table 1.4: Significant independent variables from the logistic regression analysis

Variable	Odds Ratio	95% CI	p Value
Age <12 months	1.77	1.42-2.20	<.001
Previous ICU admission	2.18	1.52-3.11	<.001
Emergency admission	1.67	1.28-2.19	<.001
CPR before admission	0.59	0.37-0.96	0.032
Admitted from another ICU or IMU	2.28	1.13-4.58	0.020
Chronic TPN	3.09	1.39-6.92	0.006
Chronic tracheostomy	2.23	1.41-3.52	0.001
Pneumonia	2.73	2.03-3.68	<.001
Other respiratory disorder	2.33	1.64-3.32	<.001
Acquired cardiac disease	3.07	2.01-4.67	<.001
Having never been discharged from hospital	2.27	1.12-4.59	0.020
Ventilator	4.59	3.60-5.86	<.001
Intracranial catheter	2.78	1.76-4.41	<.001
PRISM III-24 score between 10 and 33	2.99	2.35-3.81	<.001

CI, confidence interval; ICU, intensive care unit; CPR, Cardiopulmonary resuscitation; IMU, intermediate care unit; TPN, total parenteral nutrition; PRISM, Pediatric Risk of Mortality.

Source: Modified from Marcin et al. (2001).

In a case study carried out by Kapadia et al. (2000) in a children’s hospital in the Texas Medical Center in Houston, discrete time Markov processes was applied to study the course of stay in a PICU as the patients move back and forth between the severity of illness states. To study the dynamics of the movement of patients in PICU, PRISM scores representing the intensity of illness were utilized. The study modeled the flow of patients as a discrete time Markov process. Rather than describing by a string of services and scores, the course of treatment and length of stay in the intensive care was described as a sequence of ‘Low’, ‘Medium’ and ‘High’ severity of illness. The resulted Markovian model appeared to fit the data well. The models were expected to provide information of how the current severity of illness is likely to change over time and how long the child is likely to stay in the PICU. The use of a Markovian approach allowed estimation of the time spent by patients in different severity of illness states during the PICU stay, for the purposes of quality monitoring and resource allocation.

1.2 Studies of Length of Stay in Intensive Care Unit

According to Gruenberg et al. (2006), institutional, medical, social and psychological factors collectively affect the length of stay (LOS) in the intensive care unit (ICU). Institutional factors include geographic location, resources, organizational structure, and leadership. In term of medical factors, specific medical interventions, specific clinical laboratory values, and the type and severity of patients’ illnesses were found to be related to length of stay in the ICU. Social factors such as lack of quality communication between patients’ families and physicians or other healthcare personnel, and conflict between patients’ families and hospital staff have resulted in prolonged ICU and hospital stays. Anxiety and depression experienced by a patient’s family members are psychological characteristics that contribute to inadequate decision making and extended ICU stays.

In order to examine the impact of prolonged stay in the intensive care unit (ICU) on resource utilization, Arabi and colleagues (2002) carried out a prospective study to determine the influence of certain factors as possible predictors of prolonged stay in an adult medical/surgical ICU in a tertiary-care teaching hospital. Prolonged ICU stay was defined as length of stay >14 days. The data analyzed included the demographics and the clinical profile of each new admission. Besides, two means were used to assess severity of illness: the Acute Physiology and Chronic Health Evaluation (APACHE) II score (Knaus et al., 1985, as cited in Arabi et al., 2002) and the Simplified Acute Physiology Score (SAPS) II (Le Gall et al., 1993, as cited in Arabi et al., 2002).

The study has identified predictors found to be significantly associated with prolonged ICU stay: non-elective admissions, readmissions, respiratory or trauma-related reasons for admission, and first 24-hour evidence of infection, oliguria, coagulopathy, and the need for mechanical ventilation or vasopressor therapy had significant association with prolonged ICU stay (Table 2.5 & 2.6). It was also found that mean APACHE II and SAPS II were slightly higher in patients with prolonged stay. Arabi et al. (2002) concluded that patients with prolonged ICU stay form a small proportion of ICU patients, yet they consume a significant share of the ICU resources. Nevertheless, the outcome of this group of patients is comparable to that of shorter stay patients. The predictors identified in the study were expected to be used in targeting this group to improve resource utilization and efficiency of ICU care.

Table 1.5: Demographic and clinical profile of patients in the study group [all values shown are n (%), except where indicated otherwise]

		All (n = 947)	ICU length of stay		p value
		All (n = 947)	â‰¤ 14 days (n = 843)	>14 days (n = 104)	p value
Age (years)¹
	12-44	391 (41.3)	349 (41.4)	42 (40.4)	NS
	45-64	309 (32.6)	274 (32.5)	35 (33.7)	NS
	â‰¥65	247 (26.1)	220 (26.1)	27 (26.0)	NS
Gender
	Male	591 (62.4)	518 (61.4)	73 (70.2)	NS
	Female	356 (37.6)	325 (38.6)	31 (29.8)	NS
Type of admission
	Elective	169 (17.8)	164 (19.5)	5 (4.8)	<0.001
	Non-elective	778 (82.2)	679 (80.5)	99 (95.2)	<0.001
Severity of illness
	APACHE II score (mean ± SD)	19 ± 9	19 ± 9	21 ± 8	0.016
	SAPS II score (mean ± SD)	38 ± 20	37 ± 20	43 ± 16	0.003
	Tracheostomy	113 (11.9)	52 (6.2)	61 (58.7)	<0.001
	ICU mortality	193 (20.4)	173 (20.5)	20 (19.2)	NS

NS, not significant.

¹Because of rounding, some of the percentages may not add up to 100% exactly.

Source: Modified from Arabi et al. (2002).

Table 1.6: Possible predictors for prolonged stay and the associated odds ratio

			No. of patients (%)	ORs for prolonged stay		p value
			(n = 947)	OR	95% CI	p value
Non-elective admission			778 (82.8)	4.7	1.9-11.7	<0.001
Readmission			79 (8.3)	2.1	1.1-3.8	0.02
Main reason for admission
	Surgical
		Trauma	171 (18.1)	2.1	1.4-3.4	<0.001
		Non-trauma surgical	231 (24.4)	0.3	0.1-0.5	<0.001
	Medical
		Cardiovascular	212 (22.4)	1.0	0.6-1.6	NS
		Respiratory	159 (16.8)	2.2	1.4-3.6	<0.001
		Neurologic	36 (3.8)	0.5	0.1-2.0	NS
		Other	138 (14.6)	0.51	0.25-1.05	NS
First 24-hour data
	Coagulopathy		345 (36.4)	1.5	1.0-2.3	0.05

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