Length of stay in pediatric intensive care unit

aloofness of brook in paediatric intensifier business organization unit1.1 Scope of Review The following review of the past work through with(p) in the body politic of intensive care unit (ICU) du symmetryn of reside is divided into two parts. The first part covers the studies done on the PICU continuance of stay while the second part delves into the literature of ICU length of stay. 1.2 Studies of distance of Stay in pediatric Intensive Care Unit Ruttimann pollock (1996) investigated the relationship of length of pediatric intensive care unit (PICU) stay to insensibility of illness and other potentially relevant cyphers ope balancenal at heart 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 un collectable wreak of outliers, all patients staying foresighteder than 12 days were considered long-stay patients (4.1% of the total sample) and were excluded from the modeling-building process. In the LOS vaticination model, variables found to be importantly associated (p Table 1.1 Log-logistic regression model for length of stay variable quantityRegression coefficientSEAdjusted LOS ratio95% CIoptical prism score*0.63860.040751.281.25-1.33101.631.54-1.74151.801.67-1.94201.981.82-2.16251.621.53-1.72301.291.25-1.33401.381.33-1.44501.061.06-1.07Primary diagnosessystema nervosum centrale diseases-0.16820.02670.850.80-0.89Neoplastic diseases0.23240.05791.261.13-1.41Drug overdoses-0.17580.03830.840.77-0.90Inguinal hernia-0.32700.13440.720.55-0.94Asthma-0.11350.05270.890.80-0.99Pneumonia0.23500.04751.261.15-1.39CNS infections0.49660.05551.641.47-1.83Respiratory diseases - PRISM0.12570.05791.671.49-1.87Head trauma - PRISM0.17100.06111.731.53-1.94Diabetes - PRISM-0.33320.06661.231.08-1.40Admission conditionsPostoperative0.12670.02431.141.08-1.19Inpatient0.23580.02711.271.20-1.33 introductory ICU admission0.15620.05211.171.06-1 .29Therapy Mechanical ventilation0.49000.02581.631.55-1.72Intercept -0.01910.0278Scale2.56020.0295Log partial likelihood = -5487.2 orbiculate chi-square value = 1601.9 df = 15 p CI, self-assertion interval CNS, Central nervous system *LOS ratios computed relative to PRISM score = 0.LOS ratios computed for an interaction with PRISM score = 6.42 (sample average).Source Modified from Ruttimann Pol deprivation (1996). In the same break down, Ruttimann Pollack (1996) noted the ratio of discover to predicted LOS varied among PICUs from 0.83 to 1.25. The PICU factors associated (p Table 1.2 Effect of PICU characteristics on length of stayVariableRegression coefficientSEAdjusted LOS ratio95% CIp*Intensivist-0.12080.01890.890.85-0.920.0001Coordination-0.05130.01900.950.92-0.990.0071Residents-0.05860.02000.940.91-0.980.0033ln (PICU/ hospital beds) 0.04590.01701.031.01-1.060.0068CI, Confidence interval.*2 - ln (likelihood ratio) test.LOS ratio and 95% CIs computed for and increase of PIC U/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 malignity-of-illness assessment system, PRISM III-24 (Pediatric risk of mortality, version III, 24-hour assessment). Ruttimann et al. (1998) have beca function fusilladeted 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 Table 1.3 Generalized linear regression model (inverse Gaussian) for length of stay (n = 9558)VariableLength of stay ratio95% Confidence intervalp ValuePRISM III-240.0001(PRISM III-24)20.0001Primary diagnosesCNS infections1.411.28-1.560.0001Neoplastic diseases1.221.13-1.310.0001Asthma0.910.85-0.960.0045Pneumonia1.501.40-1.610.0001Drug overdoses0.740.70-0.790.0001CV nonoperative1.221.14-1.320.0001CV operative0.890.83-0.950.0006Dia betes0.740.67-0.810.0001Admission specificationsPostoperative0.920.88-0.960.0004Inpatient1.171.13-1.220.0001Previous ICU admission1.261.15-1.380.0001TherapyMechanical ventilation1.681.60-1.770.0001 vex intercept ( SEM) = 1.423 0.021 daysCNS, Central nervous system CV, cardiovascular system.Effect of the variable after adjusting for the effect of all other variables in the model.Log-likelihood ratio compared with the chi-squared distribution with 1 degree of freedom.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 bas ed on daily use of intensive care unit-specific therapies (based on the criterion whether on each day a patient utilize 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 Spearmans 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 get hold of the extended mischief 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 pick use of LSPs were stubborn and a prediction model was developed to identify LSPs for early quality and cost saving interventions. To name 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 keep down of health care resources and have higher mortality rates than short-stay patients.Multivariate analysis of the study place predictive factors of long-stay as age Table 1.4 Significant independent variables from the logistic regression analysisVariableOdds Ratio95% CIp ValueAge 1.771.42-2.20Previous ICU admission2.181.52-3.11Emergency admission1.671.28-2.19CPR before admission0.590.37-0.960.032Admitted from another ICU or IMU2.281.13-4.580.020Chronic total parenteral nutrition3.091.39-6.920.006Chronic tracheostomy2.231.41-3.520.001Pneumonia2.732.03- 3.68Other respiratory dis assure2.331.64-3.32Acquired cardiac disease3.072.01-4.67Having never been discharged from hospital2.271.12-4.590.020Ventilator4.593.60-5.86Intracranial catheter2.781.76-4.41PRISM III-24 score between 10 and 332.992.35-3.81CI, 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 childrens hospital in the Texas Medical Center in Houston, discrete sentence 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 meander 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 pass judgment 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 UnitAccording to Gruenberg et al. (2006), institutional, medical, social and psychological factors collectively go the length of stay (LOS) in the intensive care unit (ICU). Institutional factors include geographic location, resources, organisational 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 lag have resulted in draw out ICU and hospital stays. Anxiety and depression experienced by a patients 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. drawn-out ICU stay was defined as length of stay 14 days. The data analyzed included the demographics and the clinical profile of each new admissi on. Besides, two means were used to assess severity of illness the acuate Physiology and Chronic Health Evaluation (APACHE) II score (Knaus et al., 1985, as cited in Arabi et al., 2002) and the Simplified Acute Physiology cross off (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 answer of this gro up 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 otherwiseAll (n = 947)ICU length of stayp value 14 days (n = 843)14 days (n = 104)Age (years)12-44391 (41.3)349 (41.4)42 (40.4)NS45-64309 (32.6)274 (32.5)35 (33.7)NS65247 (26.1)220 (26.1)27 (26.0)NSGender Male591 (62.4)518 (61.4)73 (70.2)NSFemale356 (37.6)325 (38.6)31 (29.8)NSType of admissionElective169 (17.8)164 (19.5)5 (4.8)Non-elective778 (82.2)679 (80.5)99 (95.2)Severity of illnessAPACHE II score (mean SD)19 919 921 80.016SAPS II score (mean SD)38 2037 2043 160.003tracheostomy113 (11.9)52 (6.2)61 (58.7)ICU mortality193 (20.4)173 (20.5)20 (19.2)NSNS, not significant.Because of rounding, some of the percentages may not work up to 100% exactly .Source Modified from Arabi et al. (2002).Table 1.6 Possible predictors for prolonged stay and the associated odds ratioNo. of patients (%) ORs for prolonged stayp value(n = 947)OR95% CINon-elective admission778 (82.8)4.71.9-11.7Readmission79 (8.3)2.11.1-3.80.02Main reason for admissionSurgicalTrauma171 (18.1)2.11.4-3.4Non-trauma surgical231 (24.4)0.30.1-0.5MedicalCardiovascular212 (22.4)1.00.6-1.6NSRespiratory159 (16.8)2.21.4-3.6 neurologic36 (3.8)0.50.1-2.0NSOther138 (14.6)0.510.25-1.05NSFirst 24-hour dataCoagulopathy345 (36.4)1.51.0-2.30.05