Original article

Predictors for shorter and longer length of hospital stay outliers: a retrospective case-control study of 8247 patients at a university hospital trauma department

Thorsten Jentzsch, Burkhardt Seifert, Valentin Neuhaus, Rudolf M. Moos

DOI: https://doi.org/10.4414/smw.2018.14650
Publication Date: 22.08.2018
Swiss Med Wkly. 2018;148:w14650

BACKGROUND

Providing efficient healthcare is important for hospitals. Shorter and longer length of hospital stay (LOS) outliers influence financial results and reimbursement. The objective of this study was to identify independent diagnosis related group (DRG)-related risk factors for shorter and longer LOS outlier status.

METHODS

A retrospective case-control study was conducted at a Swiss level 1 trauma centre between January 2012 and December 2014. The study included all patients with available information on LOS based on DRG. Many predictor variables were tested. The outcome variable was the DRG-based LOS. Logistic regression models were fitted for shorter and longer LOS outliers, with a significance level of <1%.

RESULTS

A total of 8247 patients were analysed, of whom inliers were more frequent than shorter and longer LOS outliers (n = 5838 [70.8%] vs n = 1996 [24.2%] vs n = 413 [5.0%]). Predictors for shorter LOS outliers were death (odds ratio [OR] 4.89, 95% confidence interval [CI] 3.27–7.31), concussion (OR 4.87, 95% CI 4.20–5.63) and psychiatric disease (OR 1.85, 95% CI 1.46–2.34). Predictors for longer LOS outliers were age ≥65 years (OR 1.74, 95% CI 1.31–2.30), number of diagnoses ≥5 (OR 2.07, 95% CI 1.52–2.81), comorbidity (OR 1.75, 95% CI 1.28–2.40), number of surgical procedures (OR 1.76, 95% CI 1.36–2.28), complication perioperatively (OR 1.69, 95% CI 1.24–2.30), infection (OR 2.66, 95% CI 1.57–4.49]), concussion (OR 1.52, 95% CI 1.14–2.01) and urinary tract infection (OR 2.34, 95% CI 1.61–3.41).

CONCLUSION

This large study showed that LOS outliers, especially shorter LOS outliers, are relatively common. Patients who died, or had concussion or psychiatric disease were more commonly discharged early. Patients weremore often discharged late if they were aged ≥65 years, had more diagnoses, were comorbid, had more surgical procedures, complications perioperatively, infection, concussion and urinary tract infection. For hospitals, this can help raise awareness and lead to better management of specific diagnoses in order to avoid monetary deficits. For the public health sector, this information may be considered in future revisions of the DRG.

Keywords: length of stay (LOS) at the hospital, diagnosis related groups (DRG), outlier, inlier, trauma, fractures, complications

Introduction

The increase in healthcare costs can be more rapid than the rise in the inflation rate, which may occur despite any improvements in quality [1]. Therefore, efficient healthcare is important for hospitals. In the diagnosis related group (DRG) system, fixed prices, irrespective of actual costs, require hospitals and physicians to economise on treatment costs [2]. In Switzerland in 2012, the DRG system replaced a cost-based reimbursement system that depended on the length of hospital stay (LOS) [3]. In the United States, such a system was first introduced in New Jersey in 1980 [4]. The DRG system classifies patients into around 500 groups of diseases according to the International Classification of Diseases (ICD) and other patient characteristics.

LOS is one of the crucial points for cost containment, because inpatients who stay for a shorter (shorter LOS outlier) or longer (longer LOS outlier) period than that predicted from their respective DRG lead to less financial reimbursement. Shorter LOS outliers directly decrease financial reimbursement, whereas longer LOS outliers receive extra payments that are usually not cost-effective. This is particularly important for patients who develop in-hospital complications because they usually stay substantially longer [5]. LOS is also associated with patient outcomes. Patient satisfaction is associated with shorter LOS, and complications are linked to longer LOS [6]. However, there is a lack of information on risk factors for shorter and longer LOS outlier status in Switzerland.

The objective of this study was to identify independent DRG-related risk factors for shorter and longer LOS outlier status.

Materials and methods

Study design

Between January 2012 and December 2014, 8534 inpatients were treated and discharged (inclusion criteria) at the authors’ trauma department. Overall, 287 patients without information on LOS based on DRG were excluded, resulting in an analytical sample size of 8247 patients. All data were acquired through a search of the hospital’s routine database for billing purposes, including the disease codes of the World Health Organization. The International Statistical Classification of Diseases and Related Health Problems, 10th Revision, German Modification, Version 2010 (ICD-10) was used [7]. Prior to the start of the study, ethical approval was obtained from the local ethics committee (Kantonale Ethikkommission Zürich, KEK-ZH-Nr: 2011-0382).

Predictor variables

The main predictor variable was age. Many secondary predictor variables (and, if applicable, their ICD-10 codes) were investigated. All available data for billing purposes were included in order to provide the most comprehensive review of potential risk factors since there is very little evidence about this subject. Several, often rare, subcategories (e.g., lung contusion, hepatitis, pseudarthrosis) that would probably not add more information, but instead crowd the analysis, were excluded. By including many variables, we hoped to address various specialties and provide the basis for future studies. The included variables were gender, comorbidities, injury severity score (ISS), normalised ISS (NISS), number of surgical procedures, in-house mortality, and the presence of the following medical conditions / diagnoses described by specific ICD-10 codes as stated: perioperative complications (distinct Y-codes), number of traumatic diagnoses (all S*-Codes), number of secondary medical diagnoses, wound infection (T81.4), head concussion (S06.0), viscerocranial fracture (S02), epidural haematoma (S06.4), subdural haematoma (S06.5), subarachnoid haematoma (S06.6), multiple rib fractures (S22.4, S22.5), pneumothorax (S27.0, S27.2), liver rupture (S36.12, S36.13, S36.14, S36.15), splenic rupture (S36.02, S36.03, S36.04, S36.08), and fractures of the cervical spine (S12, S18), thoracic spine (S22.0, S22.1), lumbar spine (S32.0, S32.82), pelvic ring (S32.3, S32.5, S32.7, S32.81, S32.83, S32.89), clavicle (S42.0, S42.7), scapula (S42.1, S42.7), humerus (S42.2 – S42.4), radius (S52.1, S52.3, S52.4, S52.5, S52.6, S52.7), ulna (S52.0, S52.2), hand (S62), femoral neck (S72.0), femoral pertrochanter (S72.1, S72.2), femoral diaphysis and lower end (S72.3, S72.4), patella (S82.0), tibia (S82.1, S82.2, S82.3, S82.7), fibula (S82.11, S82.21, S82.31, S82.4, S82.6, S82.7, S82.81, S82.82), malleolus (S82.5, S82.6), foot (S92) and calcaneus (S92.0), as well as any psychiatric disease (F*), depression (F32, F31.3, F31.4, F31.5, F33.0, F33.1, F33.2, F33.3, F33.8, F33.9, F34.1), dementia (F00 - F03), coronary heart syndrome (I20 – I25), arterial hypertension (I10 – I15), arrhythmia (I47 – I49), peripheral vascular disease (I70, I79), chronic lung disease (J40-J47), thyroid disease (E00 – E07), diabetes mellitus (E10 – E14), osteoporosis (M80 – M82), malignant tumour (C*), and urinary tract infection (N30, N39.0) [8].

Outcome variables

The outcome variable was the LOS status at the hospital, based on SwissDRG. Patients were classified into three groups as shorter LOS outliers, inliers, and longer LOS outliers. Technically, every case was assigned a DRG for billing purposes, and as long as the length of stay (LOS) of the case was within the low and high margins of the DRG (inlier definition), a case was classified as LOS inlier. If the LOS was below the low trim point of the DRG, the case was defined as a shorter LOS outlier. If LOS exceeded the high trim point, the case was accordingly defined as a longer LOS outlier [9].

Statistics

Categorical values are presented as absolute number (%), and continuous values as median (interquartile range [IQR]). For differences in patient characteristics (general characteristics, truncal injuries, extremity fractures and concomitant diseases) between shorter LOS outliers, inliers, and longer LOS outliers, categorical variables were analysed with chi-squared tests and continuous variables with the Kruskal-Wallis test. In order to identify independent DRG-related risk factors for shorter and longer LOS outlier status, two multivariable logistic regression models were fitted; one with shorter LOS outliers vs inliers and another with longer LOS outliers vs inliers as the dependent variable. Confounding was assumed for gender a priori. The Wald test was used to detect differences in odds and the significance level was set at 1% owing to multiple testing. Power calculation revealed that at least 1294 patients were needed to detect differences of 5% between out- and inliers at a power of 80% and a significance level of 1%. SPSS (version 21.0, IBM Corp, Armonk, NY, USA) and Stata (version 13.1; StataCorp LLC, College Station, TX, USA) were used.

Results

General patient characteristics by LOS group

A total of 8247 patients (39.8% females; median age 49.0 years, IQR 32.0–67.0) were included in the study. All general patient characteristics differed significantly between the three LOS groups except for ISS, NISS and in-house mortality. Inliers (n = 5838, 70.8%) were more frequent than shorter LOS outliers (n = 1996, 24.2%) and longer LOS outliers (n = 413, 5.0%) (table 1).

Table 1

General patient characteristics according to the length of hospital stay (LOS), based on diagnosis related group (DRG) (n = 8247).

VariableCategoryLOS based on DRGp-value
Shorter LOS outlier
(n = 1996)
Inlier
(n = 5838)
Longer LOS outlier
(n = 413)
n or median*(% or IQR*)n or median*(% or IQR*)n or median*(% or IQR*)
Age (y) 40.0(27.0–59.0)50.0(34.0–69.0)64.0(47.0–77.0)<0.001
GenderFemale710(35.6)2388(40.9)183(44.3)<0.001
Male1286(64.4)3450(59.1)230(55.7) 
Number of diagnoses 4.0(3.0–6.0)5.0(3.0–8.0)8.0(5.0–12.0)<0.001
Number of secondary diagnoses 3.0(2.0–5.0)4.0(2.0–7.0)7.0(4.0–11.0)<0.001
ComorbidityNo1384(69.3)3065(52.5)108(26.2)<0.001
Yes612(30.7)2773(47.5)305(73.8) 
ISS 25.0(17.0–75.0)24.0(16.0–34.0)20.0(14.0–29.0)0.09
NISS 33.0(22.0–75.0)29.0(20.0–43.0)27.0(17.0–38.0)0.048
Number of surgical procedures 4.0(2.0–6.0)5.0(2.0–12.0)10.0(5.0–28.0)<0.001
Complication perioperativelyNo1921(96.2)5250(89.9)314(76.0)<0.001
Yes75(3.8)588(10.1)99(24.0) 
InfectionNo1990(99.7)5758(98.6)382(92.5)<0.001
Yes6(0.3)80(1.4)31(7.5) 
DeathNo1941(97.2)5653(96.8)408(98.8)0.06
Yes55(2.8)185(3.2)5(1.2) 

LOS = length of hospital stay; DRG = diagnosis related group; IQR = interquartile range; ISS = injury severity score; NISS = normalised injury severity score; ICU = intensive care unit
* The absolute number (%) is given for categorical data and the median (IQR) is provided for continuous data
Chi-squared test for categorical data and Kruskal-Wallis test for continuous data
There were many missing data for ISS and NISS in all three groups. The number of available patients was n = 35 for shorter LOS outliers, n = 427 for inliers, and n = 43 for longer LOS outliers.

Although males were more frequent in all three LOS groups, the proportion of males was higher among shorter LOS outliers than among inliers and longer LOS outliers (64.4 vs 59.1 vs 55.7%, p <0.001). The median age was higher in longer LOS outliers than in inliers and shorter LOS outliers (64.0 vs 50.0 vs 40.0 years, p <0.001). The same was observed for the number of diagnoses (8.0 vs 5.0 vs 4.0%, p <0.001), secondary diagnoses (7.0 vs 4.0 vs 3.0%, p <0.001) and number of surgical procedures (10.0 vs 5.0 vs 4.0, p <0.001). The proportion with patients with complications perioperatively and with wound infections was higher in longer LOS outliers than inliers and shorter LOS outliers (24.0 vs 10.1 vs 3.8%, p <0.001 and 7.5 vs 1.4 vs 0.3, p<0.001, respectively).

Truncal injuries by LOS groups

The proportion of patients with a concussion was higher in shorter LOS outliers than in longer LOS outliers and inliers (52.3 vs 23.7 vs 14.6%, p <0.001) (table 2). The proportion of patients with fractures of the viscerocranium was higher in longer LOS outliers than in shorter LOS outliers and inliers (14.5 vs 13.7 vs 13.7%, p <0.001). The proportion of patients with an intracranial haematoma was higher in inliers than longer LOS and shorter LOS outliers (2.1 vs 1.0 vs 0.1%, p <0.001 for epidural; 7.9 vs 4.8 vs 1.7%, p <0.001 for subdural; and 6.6 vs 4.6 vs 2.0%, p <0.001 for subarachnoid). The proportion of patients with multiple rib fractures and pneumothorax was higher in longer LOS outliers than inliers and shorter LOS outliers (9.0 vs 7.7 vs 2.7%, p <0.001 and 4.8 vs 3.9 vs 1.0%, p <0.001). The same was observed for liver and splenic ruptures (1.9 vs 0.7 vs 0.2%, p <0.001 and 2.2 vs 0.8 vs 0.2%, p <0.001, respectively). The proportions of patients with cervical and thoracic spine fractures was higher in inliers than longer and shorter LOS outliers (3.8 vs 2.9 vs 1.1%, p <0.001 and 5.3 vs 5.1 vs 1.4%, p <0.001). Lumbar spine fractures were more frequently observed in longer LOS outliers than inliers and shorter LOS outliers (7.5 vs 5.6 vs 1.4%, p <0.001). The same was true for pelvic ring fractures (6.8 vs 4.0 vs 0.5%, p <0.001).

Table 2

Truncal injuries according to the length of hospital stay (LOS) based on diagnosis related group (DRG) (n = 8247).

VariableCategoryLOS based on DRGp-value*
Shorter LOS outlier
(n = 1996)
Inlier
(n = 5838)
Longer
LOS outlier (n = 413)
n(%)n(%)n(%)
Head injury        
    ConcussionNo952(47.7)4987(85.4)315(76.3)<0.001
Yes1044(52.3)851(14.6)98(23.7) 
    FractureNo1723(86.3)5038(86.3)353(85.5)<0.001
Yes273(13.7)800(13.7)60(14.5) 
    Epidural haematomaNo1994(99.9)5718(97.9)409(99.0)<0.001
Yes2(0.1)120(2.1)4(1.0) 
    Subdural haematomaNo1963(98.3)5379(92.1)393(95.2)<0.001
Yes33(1.7)459(7.9)20(4.8) 
    Subarachnoid haematomaNo1956(98)5455(93.4)394(95.4)<0.001
Yes40(2.0)383(6.6)19(4.6) 
Thoracic injury        
    Multiple rib fracturesNo1942(97.3)5390(92.3)376(91.0)<0.001
Yes54(2.7)448(7.7)37(9.0) 
    PneumothoraxNo1976(99.0)5608(96.1)393(95.2)<0.001
Yes20(1.0)230(3.9)20(4.8) 
Abdominal injury        
    Liver ruptureNo1993(99.8)5800(99.3)405(98.1)<0.001
Yes3(0.2)38(0.7)8(1.9) 
    Splenic ruptureNo1993(99.8)5789(99.2)404(97.8)<0.001
Yes3(0.2)49(0.8)9(2.2) 
Spine fracture        
    CervicalNo1975(98.9)5616(96.2)401(97.1)<0.001
Yes21(1.1)222(3.8)12(2.9) 
    ThoracicNo1968(98.6)5526(94.7)392(94.9)<0.001
Yes28(1.4)312(5.3)21(5.1) 
    LumbarNo1969(98.6)5510(94.4)382(92.5)<0.001
Yes27(1.4)328(5.6)31(7.5) 
Pelvic ring fracture        
    OverallNo1987(99.5)5606(96.0)385(93.2)<0.001
Yes9(0.5)232(4.0)28(6.8) 

* Chi-squared test for categorical data

Extremity fractures by LOS groups

The proportions of patients with clavicle and scapula fractures were higher in inliers than longer and shorter LOS outliers (5.7 vs 3.9 vs 1.4%, p <0.001 and 1.9 vs 1.9 vs 0.7%, p = 0.001, respectively) (table 3). The proportion of patients with humeral fractures was higher in longer LOS outliers than inliers and shorter LOS outliers (6.8 vs 5.5 vs 1.1%, p <0.001). The proportion of patients with a radius fracture was higher in inliers than longer and shorter LOS outliers (10.0 vs 8.0 vs 3.9%, p <0.001). The proportions of ulna and hand fractures were higher in longer LOS outliers than inliers and shorter LOS outliers (4.1 vs 3.2 vs 1.5, p <0.001 and 4.6 vs 2.0 vs 1.0%, p <0.001). The proportions of patients with fractures of the femoral neck and diaphysis were higher in in longer LOS outliers than inliers and shorter LOS outliers (2.7 vs 2.2 vs 0.3%, p <0.001 and 7.0 vs 6.7 vs 1.3%, p <0.001). The proportion of patients with pertrochanteric fractures was higher in inliers than longer and shorter LOS outliers (2.0 vs 1.0 vs 0.7%, p <0.001). The proportion of patients with fractures around the knee, ankle and foot was higher in longer LOS outliers than inliers and shorter LOS outliers (2.2 vs 1.0 vs 0.1%, p <0.001 for the patella; 9.0 vs 4.7 vs 1.7, p <0.001 for the tibia; 11.9 vs 8.3 vs 3.0%, p <0.001 for the fibula; 7.0 vs 6.3 vs 2.6%, p <0.001 for the malleolus; 8.5 vs 3.5 vs 1.7%, p <0.001 for the foot; and 4.8 vs 1.4 vs 0.5%, p <0.001 for the calcaneus).

Table 3

Extremity fractures according to the length of hospital stay (LOS) based on diagnosis related group (DRG) (n = 8247).

VariableCategoryLOS based on DRGp-value*
Shorter LOS outlier
(n = 1996)
Inlier
(n = 5838)
Longer LOS outlier
(n = 413)
n(%)n(%)n(%)
Shoulder fracture        
    ClavicleNo1968(98.6)5508(94.3)397(96.1)<0.001
Yes28(1.4)330(5.7)16(3.9) 
    ScapulaNo1983(99.3)5727(98.1)405(98.1)0.001
Yes13(0.7)111(1.9)8(1.9) 
    HumerusNo1974(98.9)5517(94.5)385(93.2)<0.001
Yes22(1.1)321(5.5)28(6.8) 
Forearm fracture        
    RadiusNo1919(96.1)5255(90.0)380(92.0)<0.001
Yes77(3.9)583(10.0)33(8.0) 
    UlnaNo1966(98.5)5651(96.8)396(95.9)<0.001
Yes30(1.5)187(3.2)17(4.1) 
Hand fracture        
    OverallNo1976(99.0)5721(98.0)394(95.4)<0.001
Yes20(1.0)117(2.0)19(4.6) 
Femur fracture        
    NeckNo1991(99.7)5709(97.8)402(97.3)<0.001
Yes5(0.3)129(2.2)11(2.7) 
    PertrochantericNo1982(99.3)5721(98.0)409(99.0)<0.001
Yes14(0.7)117(2.0)4(1.0) 
    DiaphysisNo1970(98.7)5447(93.3)384(93.0)<0.001
Yes26(1.3)391(6.7)29(7.0) 
Knee fracture        
    PatellaNo1994(99.9)5780(99.0)404(97.8)<0.001
Yes2(0.1)58(1.0)9(2.2) 
    Tibia overallNo1963(98.3)5566(95.3)376(91.0)<0.001
Yes33(1.7)272(4.7)37(9.0) 
    Fibula overallNo1936(97.0)5355(91.7)364(88.1)<0.001
Yes60(3.0)483(8.3)49(11.9) 
Ankle fracture        
    MalleolusNo1945(97.4)5470(93.7)384(93.0)<0.001
Yes51(2.6)368(6.3)29(7.0) 
Foot fracture        
    OverallNo1963(98.3)5631(96.5)378(91.5)<0.001
Yes33(1.7)207(3.5)35(8.5) 
    CalcaneusNo1987(99.5)5759(98.6)393(95.2)<0.001
Yes9(0.5)79(1.4)20(4.8) 

* Chi-squared test for categorical data

Concomitant diseases by LOS groups

Regarding concomitant diseases (table 4), the proportion of patients with any psychiatric disease, depression, and dementia was higher in longer LOS outliers than inliers and shorter LOS outliers (30.5 vs 15.7 vs 15.5%, p <0.001 for any psychiatric disease; 8.2 vs 3.3 vs 2.0%, p <0.001 for depression; and 3.9 vs 1.4 vs 1.6%, p = 0.001 for dementia). The proportion of patients with a coronary heart syndrome was higher in inliers than longer and shorter LOS outliers (2.3 vs 2.2 vs 1.0%, p = 0.001). The proportion of patients with arterial hypertension, arrhythmia, peripheral vascular disease, chronic lung disease, thyroid disease, diabetes mellitus, osteoporosis, a malignant tumour, or urinary tract infection was higher in longer LOS outliers than inliers and shorter LOS outliers (34.6 vs 18.1 vs 8.9%, p <0.001; 13.1 vs 6.4 vs 2.9, p <0.001; 2.4 vs 1.3 vs 0.6%, p = 0.002; 4.6 vs 3.1 vs 1.0%, p <0.001; 3.4 vs 2.7 vs 1.1%, p <0.001; 9.4 vs 5.5 vs 3.3%, p <0.001; 6.5 vs 4.2 vs 0.8%, p <0.001; 4.6 vs 2.8 vs 1.7%, p = 0.001; 11.4 vs 3.5 vs 0.6%, p <0.001).

Table 4

Concomitant diseases according to the length of hospital stay (LOS) based on diagnosis related group (DRG) (n = 8247).

VariableCategoryLOS based on DRGp-value*
Shorter LOS outlier(n = 1996)Inlier
(n = 5838)
Longer LOS outlier
(n = 413)
n(%)n(%)n(%)
Psychiatric        
    OverallNo1687(84.5)4921(84.3)287(69.5)<0.001
Yes309(15.5)917(15.7)126(30.5) 
    DepressionNo1956(98.0)5647(96.7)379(91.8)<0.001
Yes40(2.0)191(3.3)34(8.2) 
    DementiaNo1964(98.4)5754(98.6)397(96.1)0.001
Yes32(1.6)84(1.4)16(3.9) 
Heart and vessels        
    Coronary heart syndromeNo1977(99.0)5706(97.7)404(97.8)0.001
Yes19(1.0)132(2.3)9(2.2) 
    Arterial hypertensionNo1819(91.1)4783(81.9)270(65.4)<0.001
Yes177(8.9)1055(18.1)143(34.6) 
    ArrhythmiaNo1938(97.1)5467(93.6)359(86.9)<0.001
Yes58(2.9)371(6.4)54(13.1) 
    Peripheral vascular diseaseNo1985(99.4)5763(98.7)403(97.6)0.002
Yes11(0.6)75(1.3)10(2.4) 
Lung        
    Chronic lung diseaseNo1976(99.0)5655(96.9)394(95.4)<0.001
Yes20(1.0)183(3.1)19(4.6) 
Endocrine        
    Thyroid diseaseNo1974(98.9)5678(97.3)399(96.6)<0.001
Yes22(1.1)160(2.7)14(3.4) 
    Diabetes mellitusNo1931(96.7)5515(94.5)374(90.6)<0.001
Yes65(3.3)323(5.5)39(9.4) 
    OsteoporosisNo1981(99.2)5593(95.8)386(96.5)<0.001
Yes15(0.8)245(4.2)27(6.5) 
Tumour        
    MalignantNo1963(98.3)5674(97.2)394(95.4)0.001
Yes33(1.7)164(2.8)19(4.6) 
Infection        
    Urinary tractNo1985(99.4)5633(96.5)366(88.6)<0.001
Yes11(0.6)205(3.5)47(11.4) 

*Chi-squared test for categorical data

Independent risk factors for shorter LOS outliers

There were 17 independent protective factors for shorter LOS outliers regarding LOS (table 5): comorbidity (odds ratio [OR] 0.46, 95% confidence interval [CI] 0.38–0.56; p <0.001), number of surgical procedures (OR 0.55, 95% CI 0.48–0.62; p <0.001), complication perioperatively (OR 0.58, 95% CI 0.44–0.76; p <0.001), epidural haematoma (OR 0.10, 95% CI 0.02–0.43; p = 0.002), subdural haematoma (OR 0.29, 95% CI 0.18–0.45; p <0.001), cervical spine fracture (OR 0.34, 95% CI 0.20–0.56; p <0.001), thoracic spine fracture (OR 0.46, 95% CI 0.30–0.71; p <0.001), lumbar spine fracture (OR 0.35, 95% CI 0.23–0.54, p <0.001), pelvic ring fracture (OR 0.20, 95% CI 0.10–0.39; p <0.001), clavicle fracture (OR 0.20, 95% CI 0.13–0.31; p <0.001), humerus fracture (OR 0.20, 95% CI 0.13–0.32; p <0.001), radius fracture (OR 0.35, 95% CI 0.27–0.46; p <0.001), diaphyseal femur fracture (OR 0.21, 95% CI 0.09–0.45; p <0.001), patella fracture (OR 0.11, 95% CI 0.03–0.47; p = 0.003), tibial fracture (OR 0.49, 95% CI 0.31–0.76; p = 0.002), osteoporosis (OR 0.42, 95% CI 0.24–0.74; p = 0.003) and urinary tract infection (OR 0.31, 95% CI 0.16–0.61; p = 0.001).

Table 5

Logistic regression model for factors associated with shorter LOS outliers regarding length of hospital stay (LOS) (n = 6250).

VariableAdjusted OR*(Adjusted 95% CI*)p-value
Age ≥65 years0.87(0.73–1.03)0.113
Female gender1.10(0.97–1.25)0.154
Number of diagnoses ≥50.87(0.75–1.02)0.093
Comorbidity0.46(0.38–0.56)<0.001
Number of surgical procedures0.55(0.48–0.62)<0.001
Complication perioperatively0.58(0.44–0.76)<0.001
Infection0.67(0.27–1.63)0.376
Death4.89(3.27–7.31)<0.001
Concussion4.87(4.20–5.63)<0.001
Fracture0.86(0.70–1.06)0.148
Epidural haematoma0.10(0.02–0.43)0.002
Subdural haematoma0.29(0.18–0.45)<0.001
Subarachnoid haematoma0.78(0.51–1.18)0.234
Multiple rib fractures0.66(0.46–0.94)0.020
Pneumothorax0.59(0.34–1.02)0.057
Liver rupture0.65(0.17–2.53)0.535
Splenic rupture0.50(0.14–1.74)0.275
Cervical spine fracture0.34(0.20–0.56)<0.001
Thoracic spine fracture0.46(0.30–0.71)<0.001
Lumbar spine fracture0.35(0.23–0.54)<0.001
Pelvic ring fracture overall0.20(0.10–0.39)<0.001
Clavicle fracture0.20(0.13–0.31)<0.001
Scapula fracture0.90(0.43–1.87)0.779
Humerus fracture0.20(0.13–0.32)<0.001
Radius fracture0.35(0.27–0.46)<0.001
Ulna fracture0.69(0.45–1.05)0.084
Hand fracture overall0.54(0.31–0.95)0.032
Femoral neck fracture0.72(0.22–2.41)0.599
Pertrochanteric fracture1.69(0.63–4.52)0.294
Diaphyseal femur fracture0.21(0.09–0.45)<0.001
Patella fracture0.11(0.03–0.47)0.003
Tibial fracture overall0.49(0.31–0.76)0.002
Fibula fracture overall0.62(0.35–1.08)0.091
Malleolar fracture0.54(0.30–0.97)0.041
Foot fracture overall0.56(0.35–0.89)0.014
Calcaneal fracture0.89(0.37–2.11)0.790
Psychiatric disease overall1.85(1.46–2.34)<0.001
Depression0.62(0.41–0.95)0.027
Dementia1.54(0.91–2.60)0.106
Coronary heart syndrome0.96(0.55–1.67)0.879
Arterial hypertension0.96(0.75–1.22)0.727
Arrhythmia1.03(0.72–1.46)0.887
Peripheral vascular disease1.22(0.59–2.54)0.596
Chronic lung disease0.73(0.43–1.24)0.243
Thyroid disease0.73(0.44–1.21)0.217
Diabetes mellitus1.24(0.90–1.73)0.194
Osteoporosis0.42(0.24–0.74)0.003
Malignant tumour1.28(0.84–1.95)0.245
Urinary tract infection0.31(0.16–0.61)0.001

OR = odds ratio; CI = confidence interval
* Adjusted for all variables shown in the table given in the reference category of all other variables
† Wald test

Three independent risk factors were identified for shorter LOS outliers regarding LOS: death (OR 4.89, 95% CI 3.27–7.31; p <0.001), concussion (OR 4.87, 95% CI 4.20–5.63; p <0.001) and psychiatric disease (OR 1.85, 95% CI 1.46–2.34; p <0.001).

Independent risk factors for longer LOS outliers

There were two independent protective factors for longer LOS outliers regarding LOS (table 6): subdural haematoma (OR 0.47, 95% CI 0.27–0.80; p = 0.006) and cervical spine fracture (OR 0.42, 95% CI 0.22–0.79; p = 0.007).

Table 6

Logistic regression model for factors associated with longer LOS outliers regarding length of hospital stay (LOS) (n = 6250).

VariableAdjusted OR*(Adjusted 95% CI*)p-value
Age ≥65 years1.74(1.31–2.30)<0.001
Female gender1.08(0.85–1.37)0.516
Number of diagnoses ≥52.07(1.52–2.81)<0.001
Comorbidity1.75(1.28–2.40)0.001
Number of surgical procedures1.76(1.36–2.28)<0.001
Complication perioperatively1.69(1.24–2.30)0.001
Infection2.66(1.57–4.49)<0.001
Death0.32(0.12–0.82)0.017
Concussion1.52(1.14–2.01)0.004
Fracture1.04(0.73–1.47)0.837
Epidural haematoma0.61(0.21–1.75)0.358
Subdural haematoma0.47(0.27–0.80)0.006
Subarachnoid haematoma0.66(0.37–1.17)0.157
Multiple rib fractures0.72(0.46–1.14)0.161
Pneumothorax1.00(0.55–1.82)0.988
Liver rupture1.81(0.70–4.68)0.223
Splenic rupture1.51(0.64–3.51)0.344
Cervical spine fracture0.42(0.22–0.79)0.007
Thoracic spine fracture0.76(0.46–1.25)0.278
Lumbar spine fracture0.86(0.54–1.35)0.505
Pelvic ring fracture overall1.15(0.73–1.81)0.539
Clavicle fracture1.05(0.60–1.84)0.871
Scapula fracture0.92(0.40–2.11)0.847
Humeral fracture0.94(0.60–1.45)0.767
Radius fracture0.88(0.58–1.33)0.542
Ulna fracture1.29(0.72–2.33)0.391
Hand fracture overall1.91(1.11–3.31)0.020
Femoral neck fracture1.06(0.44–2.59)0.894
Pertrochanteric fracture0.39(0.12–1.32)0.130
Diaphyseal femur fracture0.64(0.34–1.19)0.156
Patella fracture1.83(0.82–4.09)0.138
Tibial fracture overall1.79(1.05–3.05)0.033
Fibula fracture overall1.10(0.60–2.03)0.757
Malleolar fracture1.24(0.63–2.43)0.532
Foot fracture overall1.84(0.98–3.44)0.057
Calcaneal fracture1.66(0.74–3.72)0.218
Psychiatric disease overall1.11(0.80–1.52)0.533
Depression1.55(0.98–2.46)0.062
Dementia1.37(0.73–2.58)0.327
Coronary heart syndrome0.53(0.26–1.09)0.085
Arterial hypertension1.21(0.91–1.60)0.187
Arrhythmia1.08(0.76–1.53)0.685
Peripheral vascular disease0.98(0.47–2.03)0.960
Chronic lung disease0.83(0.49–1.40)0.486
Thyroid disease0.75(0.42–1.35)0.336
Diabetes mellitus0.96(0.65–1.41)0.836
Osteoporosis0.84(0.54–1.32)0.448
Malignant tumour0.93(0.55–1.56)0.782
Urinary tract infection2.34(1.61–3.41)<0.001

OR = odds ratio; CI = confidence interval
* Adjusted for all variables shown in the table given in the reference category of all other variables
† Wald test

Eight independent risk factors were identified for longer LOS outliers regarding LOS: age ≥65 years (OR 1.74, 95% CI 1.31–2.30; p <0.001), number of diagnoses ≥5 (OR 2.07, 95% CI 1.52–2.81; p <0.001), comorbidity (OR 1.75, 95% CI 1.28–2.40; p = 0.001), number of surgical procedures (OR 1.76, 95% CI 1.36–2.28; p <0.001), complication perioperatively (OR 1.69, 95% CI 1.24–2.30; p = 0.001), infection (OR 2.66, 95% CI 1.57–4.49; p <0.001), concussion (OR 1.52, 95% CI 1.14–2.01; p = 0.004) and urinary tract infection (OR 2.34, 95% CI 1.61–3.41; p <0.001).

Discussion

Although some reports [4, 913] have focused on risk factors for monetary deficits according to DRGs from diverse departments in hospitals, very little is known about independent risk factors for shorter LOS and longer LOS outliers according to SwissDRG in a trauma department. The present study showed that outliers are relatively common in trauma patients. In our study, shorter LOS outliers were more frequent than longer LOS outliers (24.2 vs 5.0%). However, we identified three independent risk factors (death, concussion, and psychiatric disease) for shorter LOS outliers. On the other hand, there were eight independent risk factors (age ≥65 years, number of diagnoses ≥5, comorbidity, number of surgical procedures, complication perioperatively, infection, concussion and urinary tract infection) for longer LOS outliers.

A previous Portuguese study of 9,253,087 patients from diverse hospital departments found the proportion of longer LOS outliers to be 3.9%, and reported age, type of admission and hospital type to be significantly associated with longer LOS [10]. Our slightly higher proportion of 5.0% in a trauma department is, therefore, probably influenced by our teaching University Hospital status and cohort demographics [11].

In terms of costs, a previous study of 28,893 cases discharged from diverse departments of our hospital found psychiatric disease, admission as an emergency case and admission from an external healthcare provider to be significant predictors for higher monetary deficits [12]. Regarding LOS, our study suggests that these results for monetary deficits may be in line with discharges from our trauma department, where psychiatric disease was an independent risk factor for shorter LOS outlier status [14]. A potential explanation is that these patients may have been transferred to a specialised psychiatric unit. However, since we used data from a routine database without opening individual patient charts, this cannot be answered for sure. Another study of 23,098 patients in the American College of Surgeon’s National Surgical Quality Improvement Program (ACS NSQIP) found that the median LOS was 16.1 days in patients with complications compared with 5 days in patients without complications [1]. These results are backed up by our results, where complications perioperatively were an independent risk factor for longer LOS outlier status. Furthermore, concussion was not only a risk factor for shorter, but also for longer LOS. This may be explained by the fact that patients with mild concussions may have been discharged early, whereas those with severe concussion (or even additional diagnoses) may have been discharged late.

Aside from its retrospective nature, a limitation of our study is that the data for the identification of independent DRG-related risk factors were exclusively obtained from ICD codes instead of chart review. This may have occasionally led to misclassification of certain variables. However, coding is always done with care, not only because financial reimbursement relies on obtaining the most accurate information. The information about the lack of differences for ISS and NISS must be interpreted with caution. Both variables included many missing data and the number of patients analysed was low. This study included many variables as potential risk factors. Correlations between variables may exist. However, the goal of this study was to provide a first insight into the topic, and, by fitting regression models, ORs are always given in the reference category of all other variables. Future studies will be able to focus on fewer variables in more detail and/or further subcategories as well.

The introduction of DRGs and the modification from retro- to prospective payments has led to a shift in risks of monetary loss from insurers to healthcare providers, who are now forced to economise on treatment costs [2]. Shorter LOS outliers are usually accepted by hospitals as they are still cost-covering, whereas longer LOS outliers are only reimbursed with a smaller than 50% rebate on average daily costs and usually lead to a financial deficit [2]. The results of our study could be helpful to hospitals and physicians since patients with risk factors for outlier status can be counselled and managed appropriately (of course, keeping in mind that providing the best patient care for each patient independent of monetary considerations is of utmost importance for each physician). Although physicians are aware that there are out- and inliers, the recommended LOS times are usually unknown. It seems important that regular training is implemented in the routine schedule of physicians to optimise costs for healthcare providers. An additional option is to integrate the optimal LOS in electronic chart systems in order to keep treating physicians up-to-date. Since LOS can be influenced by hospitals and patients alike, this may ultimately reduce costs for hospitals, insurers and patients. Future revisions of the DRG may also take this knowledge into consideration.

Conclusion

Our study showed that outliers, especially shorter LOS outliers, are relatively common. There were several predictors for outliers. Patients who died, or had concussion or psychiatric disease were more commonly discharged early. Patients were more often discharged latedischarged late if they were aged ≥65 years, had more diagnoses, were comorbid, had more surgical procedures, complications perioperatively, infection, concussion and urinary tract infection. For hospitals, this can help raise awareness and lead to better management of specific diagnoses in order to avoid monetary deficits. For the public health sector, this information may be considered in future revisions of the DRG.

1 Cohen ME, Bilimoria KY, Ko CY, Richards K, Hall BL. Variability in length of stay after colorectal surgery: assessment of 182 hospitals in the national surgical quality improvement program. Ann Surg. 2009;250(6):901–7. doi:. http://dx.doi.org/10.1097/SLA.0b013e3181b2a948 PubMed

2 Felder S. The variance of length of stay and the optimal DRG outlier payments. Int J Health Care Finance Econ. 2009;9(3):279–89. doi:. http://dx.doi.org/10.1007/s10754-008-9051-1 PubMed

3 Swiss DRG. Wichtige Begriffe: Grenzverweildauer und Ausreisser [cited 2017 04 November]. Available from: http://www.swissdrg.org/de/02_informationen_swissDRG/wichtige_begriffe.asp?navid = 16.

4 Hsiao WC, Sapolsky HM, Dunn DL, Weiner SL. Lessons of the New Jersey DRG payment system. Health Aff (Millwood). 1986;5(2):32–45. doi:. http://dx.doi.org/10.1377/hlthaff.5.2.32 PubMed

5 Tecklenburg A, Liebeneiner J, Schaefer O. Ausreißerfälle in den operativen Disziplinen [Outlier cases in surgical disciplines. Micro-economic and macro-economic problems]. Chirurg. 2009;80(9):768–72. Article in German. doi:. http://dx.doi.org/10.1007/s00104-009-1693-0 PubMed

6 Tokunaga J, Imanaka Y. Influence of length of stay on patient satisfaction with hospital care in Japan. Int J Qual Health Care. 2002;14(6):493–502. doi:. http://dx.doi.org/10.1093/intqhc/14.6.493 PubMed

7 World Health Organization (WHO). International Statistical Classification of Diseases and Related Health Problems (ICD-10) [cited 2017 04 November]. Available from: http://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf.

8 Jentzsch T, Neuhaus V, Seifert B, Osterhoff G, Simmen HP, Werner CM, et al.The impact of public versus private insurance on trauma patients. J Surg Res. 2016;200(1):236–41. doi:. http://dx.doi.org/10.1016/j.jss.2015.06.055 PubMed

9 Moos RM, Sprengel K, Jensen KO, Jentzsch T, Simmen HP, Seifert B, et al.Reimbursement of care for severe trauma under SwissDRG. Swiss Med Wkly. 2016;146:w14334. doi:. http://dx.doi.org/10.4414/smw.2016.14334 PubMed

10 Freitas A, Silva-Costa T, Lopes F, Garcia-Lema I, Teixeira-Pinto A, Brazdil P, et al.Factors influencing hospital high length of stay outliers. BMC Health Serv Res. 2012;12(1):265. doi:. http://dx.doi.org/10.1186/1472-6963-12-265 PubMed

11 Cots F, Mercadé L, Castells X, Salvador X. Relationship between hospital structural level and length of stay outliers. Implications for hospital payment systems. Health Policy. 2004;68(2):159–68. doi:. http://dx.doi.org/10.1016/j.healthpol.2003.09.004 PubMed

12 Mehra T, Müller CT, Volbracht J, Seifert B, Moos R. Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center. PLoS One. 2015;10(10):e0140874. doi:. http://dx.doi.org/10.1371/journal.pone.0140874 PubMed

13 Motte S, Mélot C, Di Pierdomenico L, Martins D, Leclercq P, Pirson M. Predictors of costs from the hospital perspective of primary pulmonary embolism. Eur Respir J. 2016;47(1):203–11. doi:. http://dx.doi.org/10.1183/13993003.00281-2015 PubMed

14 Swiss DRG. SwissDRG Version 7.0 und Systempräsentation 2017 [cited 2018 11 February]. Available from: https://download.swissdrg.org/sp2017_xo3wtrv2.

Jentzsch Thorstena, Seifert Burkhardtb, Neuhaus Valentina, Moos Rudolf M.c

a Division of Trauma Surgery, Department of Surgery, University Hospital Zurich, University of Zurich, Switzerland

b Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Switzerland

c Medical Directorate, University Hospital Zurich, University of Zurich, Switzerland

TJ: statistical analysis and interpretation of data, drafting the manuscript; BS: statistical analysis; VN: ethics approval, acquisition and interpretation of data, drafting the manuscript RMM: acquisition and interpretation of data; all: revision of the manuscript, final approval of the version to be published.

We would like to thank Karina Fischer, PhD, for her review of this manuscript. The abstract was presented as a poster presentation at the annual conference of Swiss Orthopaedics, Montreux, Switzerland, June 6 to 8, 2018.

No financial support and no other potential conflict of interest relevant to this article was reported.

Thorsten Jentzsch, MD, MSc Epidemiology, Department of Trauma, University Hospital Zurich, University of Zurich, Ramistrasse 100, CH-8091 Zurich, thorsten.jentzsch[at]gmail.com

length of stay (LOS) at the hospital, diagnosis related groups (DRG), outlier, inlier, trauma, fractures, complications