Introduction
Leukemia is a hematologic malignancy that poses a significant
challenge due to its rapid proliferation of immature blood cells,
affecting individuals of all ages. As of January 1, 2019, there were
an estimated 166,412 people with leukemia in the United States,
based on diagnoses made between 2014 and 2018. This statistic underscores the significance of leukemia as a public health
concern [1]. Thus, it is important to examine deeper into the factors that shape prognosis and mortality among those diagnosed
with this condition.
Research has indicated that malnutrition is prevalent in 30-
85% of individuals diagnosed with cancer, thus corroborating it to
be a critical factor that influences both disease progression and
patient outcomes [2,3]. The roots of malnutrition in acute leukemia are multifaceted, stemming from the disease’s metabolic
demands, treatment-related side effects, and inadequate dietary intake. This nutritional deficit can exacerbate disease-related
complications, compromising the immune system, impeding the
healing process, and diminishing the body’s capacity to tolerate
and respond to treatment [4].
Previous studies have established a broad connection between
poor nutritional status and undesirable outcomes, such as prolonged hospital stays, increased admission rates, and elevated cancer-related mortality [5,6]. Nutritional status is assessed by considering six potential indicators of malnutrition, with a diagnosis of
malnutrition deemed appropriate if two or more criteria are met:
insufficient energy intake, weight loss, loss of muscle mass, loss of
subcutaneous fat, localized or generalized fluid retention, and diminished functional capacity measured through hand grip strength
[7]. However, despite its undeniable significance, the precise impact of malnutrition on acute leukemia’s progression and treatment outcomes remains an area in need of further exploration.
To bridge this knowledge gap, this retrospective study leveraged data from a large cohort of hospitalized acute leukemia
patients in the United States. Our analysis included patients hospitalized with acute myeloid leukemia or acute lymphocytic leukemia. The aim was to comprehensively assess the impact of malnutrition on disease progression and clinical outcomes, ultimately
shedding light on this critical aspect of leukemia care.
Materials and methods
Data source: Our study relied on the data provided by the NIS
database, which was developed as an integral component of the
Healthcare Cost and Utilization Project (HCUP), generously sponsored by the Agency for Healthcare Research and Quality (AHRQ).
This massive all-payer inpatient healthcare database offers a
wealth of public information to researchers and boasts an impressive sample size that approximates 20% of stratified discharges
from community hospitals across America [8]. Employing a systematic sampling design, this comprehensive resource is crafted
from state-initiated patient databases, generating distinctive discharge records that encompass vital medical information. These
records include primary and secondary diagnoses, alongside procedures conducted during hospitalization. Additionally, each record incorporates demographic details, comorbidities, severity of
illness, and mortality risk based on All Patient Refined DiagnosisRelated Groups (APR-DRG). Other incorporated elements consist of the Length of Hospital Stay (LOS), teaching status, hospital location, geographic region, and an estimated median household
income quartile based on the patient’s zip code. Furthermore,
primary payer information, discharge disposition, and in-hospital
mortality are meticulously documented in each record.
Study design and population: Since Leukemia is often diagnosed in childhood but can also affect individuals of all ages,
our study encompassed patients both below and above 18 years
old. The National Inpatient Sample for 2019-2020 was analyzed to
specifically identify patients who had been admitted with a diagnosis of Acute Myeloid or Lymphocytic Leukemia. Subsequently,
the patient cohort was segregated into two distinct groups based
on their respective nutritional statuses: one group comprising individuals without malnutrition and the other including those with
concurrent malnutrition.
Outcomes: The primary objective of the study was to evaluate
and contrast mortality rates between two groups. In addition to
this vital outcome measure, various secondary parameters, such
as length of hospitalization and total hospital charges, were examined to gain a more comprehensive understanding of patient
outcomes. These metrics provided insights into resource utilization patterns within each population. Furthermore, the study
compared the Charlson comorbidity index, a tool capable of addressing numerous confounding factors, between patients with
and without concomitant frailty.
Statistical analysis: The statistical analysis in this study prioritized ensuring the reliability and validity of results. We utilized
Stata 17 software with weighted samples to align with Healthcare
Cost and Utilization Project regulations when using the NIS database for national estimates. Continuous variables were represented by mean values and standard deviations, while categorical
variables were expressed as percentages. For group comparisons, the «Student’s t-test» on Stata was applied to continuous
variables, and the «Chi-square test» was used for categorical variables. The multivariate analysis carefully considered variables
that showed significant differences in outcomes from univariate
analysis (p-value less than 0.2) and also included crucial determinants regardless of their statistical significance. It involved calculating odds ratios for all outcomes, adjusting for factors such
as age, gender, ethnicity, insurance coverage status, hospital location, teaching status, bed size, and the Charlson comorbidity
index to provide a comprehensive understanding of the data.
Results
A total of 24855 patients with the primary discharge diagnosis of acute Leukemia were included in the study. Among these,
3425(13.7%) had concomitant malnutrition, while the remaining
21430 had no malnutrition. The mean age of patients with Leukemia and malnutrition was 43.15 years (+/-25.80) compared to
35.26 years (+/-26.28) for those without malnutrition (p<0.001).
Younger patients (<18 years) had fewer instances of malnutrition
(22.04% vs. 35.49%, p<0.001), whereas older patients exhibited a
higher prevalence of malnutrition (Aged 46-64: 23.8% vs 18.57%,
Aged>65: 26.57% vs 18.88%, p<0.001). A more significant proportion of patients diagnosed with malnutrition were found to
be covered by Medicare (28.75% vs 21.44%, p<0.001), whereas
those without malnutrition exhibited a higher prevalence of Medicaid insurance (31.41% vs 24.11%, p<0.001). The representation of individuals with private insurance was nearly equivalent
across both groups (43.12% vs 44.95%, p<0.001). Surprisingly,
there was a higher prevalence of people without insurance in
the group without malnutrition (4.04% vs 3.09%, p<0.001). Acute
Leukemia with Malnutrition had a larger proportion of patients
with fluid and electrolyte disorders (50.36% vs 31.68%, <0.001).
A higher proportion of heart failure was noted in patients with
malnutrition (8.03% vs 5.04%, p=0.001). Leukemia without malnutrition had fewer patients who underwent Hematopoietic stem
cell transplant than patients with malnutrition (10.69% vs 20.15%,
p<0.001). Conversely, a larger proportion of patients without
malnutrition had undergone chemotherapy (2.66% vs 0.73%, p=
0.002). A larger percentage of patients with malnutrition were discharged to homes with home health services (23.43% vs. 13.61%,
p<0.001), while patients without malnutrition had a large percentage that was discharged to homes and skilled nursing facilities
(75.74% vs. 70.85% & 9.9% vs. 5.35%, p<0.001). Smaller hospitals
had a higher proportion of patients without malnutrition (14.28%
vs 9.78%, p<0.001), while larger hospitals had a greater percentage of patients with malnutrition (78.54% vs 64%, p<0.001). Additionally, non-teaching hospitals tended to have fewer patients
with malnutrition and more patients without malnutrition (2.77%
vs 5.76%, p=0.002) (Table 1).
Table 1: Comparison of baseline characteristics of acute leukemia
patients with and without malnutrition.
|
Acute Leukemia without Malnutrition
(%)
|
Acute Leukemia with Malnutrition
(%)
|
P-Value |
No. of patients |
21430 (%) |
3425 (%) |
|
Patient characteristics |
|
|
|
Gender (%) |
|
|
0.133 |
Male |
11941 (55.72) |
2005 (58.54) |
|
Female |
9489 (44.28) |
1420 (41.46) |
|
Age |
|
|
|
Mean age (SD) |
35.26 (26.28) |
43.15 (25.80) |
<0.001 |
Age distribution (%) |
|
|
<0.001 |
<18 |
7606 (35.49) |
755 (22.04) |
|
18-35 |
3915 (18.27) |
680 (19.85) |
|
36-45 |
1886 (8.8) |
265 (7.74) |
|
46-64 |
3980 (18.57) |
815 (23.8) |
|
>65 |
4046 (18.88) |
910(26.57%) |
|
Race (%) |
|
|
0.06 |
White |
12166 (56.77) |
2077 (60.65) |
|
Black |
1875 (8.75) |
376 (10.97) |
|
Hispanic |
6187 (28.87) |
801 (23.39) |
|
Other |
1202 (5.61) |
171 (5) |
|
Median household income
national quartile for
patient zip code (%)
|
|
|
0.343 |
$1-$49,999 |
5606 (26.16) |
873 (25.48) |
|
$50,000-$64,999 |
5085 (23.73) |
893 (26.08) |
|
$65,000-$85,999 |
5780 (26.97) |
832 (24.29) |
|
>$86,000 |
4959 (23.14) |
827 (24.14) |
|
Charlson comorbidity index
(%)
|
|
|
<0.001 |
2 |
13516 (63.07) |
1870 (54.6) |
|
3 or more |
7914 (36.93) |
1555 (45.4) |
|
Insurance provider (%) |
<0.001 |
|
|
Medicare |
4595 (21.44) |
985 (28.75) |
|
Medicaid |
6731 (31.41) |
826 (24.11) |
|
Private |
9241 (43.12) |
1509 (44.05) |
|
Uninsured |
866 (4.04) |
106 (3.09) |
|
Comorbidities (%) |
|
|
|
Hypertension |
6285 (29.33) |
985 (28.76) |
0.751 |
Diabetes mellitus |
2959 (13.81) |
465 (13.58) |
0.87 |
Fluid and electrolyte |
|
|
|
disorders |
6789 (31.68) |
1725 (50.36) |
<0.001 |
Chronic kidney disease |
|
|
|
CKD2 |
124 (0.58) |
30 (0.88) |
0.291 |
CKD3 |
446 (2.08) |
65 (1.9) |
0.758 |
CKD4 |
75 (0.35) |
5 (0.15) |
0.367 |
CKD5 |
25 (0.12) |
4(0.11) |
0.689 |
CKD Unspecified |
371 (1.73) |
80 (2.34) |
0.201 |
ESRD |
71 (0.33) |
20 (0.58) |
0.303 |
Hyperlipidemia (HLD) |
3109 (14.51) |
540 (15.77) |
0.432 |
Obesity |
2246 (10.48) |
280 (8.18) |
0.1 |
COPD |
600 (2.8) |
120 (3.5) |
0.296 |
Heart Failure |
1080 (5.04) |
275 (8.03) |
0.001 |
Coronary artery disease |
1354 (6.32) |
320 (9.34) |
0.0213 |
History of CVA |
56 (0.26) |
20 (0.58) |
0.147 |
Treatment |
|
|
|
HSCT |
2291 (10.69) |
690 (20.15) |
<0.001 |
Chemotherapy |
570 (2.66) |
25 (0.73) |
0.002 |
Discharge disposition (%)
|
|
|
<0.001 |
Home |
16231 (75.74 ) |
2427 (70.85) |
|
Home with home health |
2917 (13.61 ) |
802 (23.43) |
|
Skilled nursing facility
|
2122 (9.9 ) |
183 (5.35) |
|
Against Medical Advice |
163 (0.76 ) |
13 (0.37) |
|
Hospital characteristics (%)
|
|
|
|
Bed size of hospital
(STRATA)
|
<0.001 |
|
|
Small |
3060 (14.28) |
335 (9.78) |
|
Medium |
4655 (21.72) |
400 (11.68) |
|
Large |
13715 (64) |
2690 (78.54) |
|
Hospital location |
|
|
0.023 |
Rural |
281 (1.31) |
10 (0.29) |
|
Urban |
21149 (98.69) |
3415 (99.71) |
|
Hospital teaching status
|
|
|
0.002 |
Non-teaching hospital |
1234 (5.76) |
95 (2.77) |
|
Teaching hospital |
20196 (94.24) |
3330 (97.23) |
|
Region of hospital |
|
|
0.309 |
Northeast |
3521 (16.43) |
675 (19.71) |
|
Midwest |
3800 (17.73) |
670 (19.56) |
|
South |
8855 (41.32) |
1420 (41.46) |
|
West |
5255 (24.52) |
660 (19.27) |
|
SD: Standard Deviation; CVA: Cardiovascular Accident; HSCT:
Hematopoietic Stem Cell Transplant; COPD: Chronic Obstructive
Pulmonary Disease; ESRD: End Stage Renal Disease.
Table 2: Univariate and multivariate regression analysis of odds of mortality in acute leukemia patients with and without malnutrition.
|
Univariate regression
|
Multivariate regression
|
Mortality |
Odds ratio |
95% Conf. Interval |
P-Value |
Odds ratio |
95% Conf. Interval |
P- Value |
Malnutrition |
3.33 |
(2.49-4.45) |
<0.001 |
2.89 |
(2.11-3.94) |
<0.001 |
Gender (%) |
|
|
|
|
|
|
Male |
Reference |
Female |
0.96 |
(0.73-1.24) |
0.766 |
1.05 |
(0.78-1.41) |
0.713 |
Age |
|
|
|
|
|
|
Mean age |
1.17 |
(1.34-1.66) |
0.001 |
1.03 |
(1.00-1.05) |
0.012 |
Age distribution (%) |
|
|
|
|
|
|
<18 |
Reference |
18-35 |
2.22 |
(1.41-3.48) |
0.001 |
1.84 |
(1.08-3.15) |
0.025 |
36-45 |
2.04 |
(1.18-3.51) |
0.01 |
1.59 |
(0.85-2.96) |
0.142 |
46-64 |
2.68 |
(1.75-4.12) |
<0.001 |
1.74 |
(1.01-2.97) |
0.043 |
>65 |
5.4 |
(3.75-7.78) |
<0.001 |
2.76 |
(1.43-5.31) |
0.002 |
Race(%) |
|
|
|
|
|
|
White Reference |
|
|
|
|
|
|
Black |
1.62 |
(1.06-2.48) |
0.025 |
1.74 |
(1.11-2.74) |
0.015 |
Hispanic |
0.69 |
(0.49-0.96) |
0.031 |
0.84 |
(0.58-1.23) |
0.39 |
Other |
1.23 |
(0.70-2.14) |
0.465 |
1.49 |
(0.82-2.72) |
0.187 |
Median household income
national quartile for
patient zip code (%)
|
$1-$49,999 |
Reference |
$50,000-$64,999 |
0.94 |
(0.65-1.37) |
0.783 |
0.93 |
(0.60-1.42) |
0.747 |
$65,000-$85,999 |
1.16 |
(0.81-1.67) |
0.397 |
1.26 |
(0.83-1.91) |
0.267 |
>$86,000 |
0.92 |
(0.63-1.36) |
0.712 |
0.88 |
(0.56-1.37) |
0.58 |
Charlson comorbidity index
(%)
|
|
|
|
|
|
|
2 |
Reference |
3 or more |
4 |
(3.03-5.49) |
<0.001 |
3.1 |
(2.20- 4.38) |
<0.001 |
Insurance provider (%) |
|
|
|
|
|
|
Medicare Reference |
|
|
|
|
|
|
Medicaid |
0.35 |
(0.25-0.50) |
<0.001 |
1.23 |
(0.68-2.20) |
0.482 |
Private |
0.4 |
(0.30-0.54) |
<0.001 |
0.98 |
(0.60-1.62) |
0.96 |
Uninsured |
0.36 |
(0.15-0.85) |
0.021 |
1.18 |
(0.45-3.06) |
0.725 |
Comorbidities (%) |
|
|
|
|
|
|
Hypertension |
1.04 |
(0.79-1.35) |
0.767 |
1.04 |
(0.74-1.46) |
0.819 |
Diabetes mellitus |
1.31 |
(0.93-1.84) |
0.116 |
0.54 |
(0.36-0.81) |
0.003 |
Fluid and electrolyte
disorders
|
3.18 |
(2.44-4.14) |
<0.001 |
2.43 |
(1.81-3.27) |
<0.001 |
Chronic kidney disease |
|
|
|
|
|
|
CKD2 |
Reference |
CKD3 |
3.21 |
(1.77-5.79) |
<0.001 |
1.32 |
(0.63-2.76) |
0.447 |
CKD4 |
4.16 |
(1.17-14.84) |
0.028 |
0.87 |
(0.13-5.47) |
0.883 |
CKD5 |
3.19 |
(1.32-6.84) |
0.0325 |
1.52 |
(0.28-3.12) |
0.325 |
CKD Unspecified |
2.02 |
(0.94-4.33) |
0.07 |
0.78 |
(0.30-2.03) |
0.618 |
ESRD |
7 |
(2.46-19.84) |
<0.001 |
3.47 |
(1.18-10.12) |
0.023 |
Hyperlipidemia (HLD) |
1.39 |
(1.01-1.90) |
0.039 |
0.66 |
(0.43-1.00) |
0.05 |
Obesity |
1.19 |
(0.80-1.77) |
0.38 |
1.07 |
(0.69-1.65) |
0.744 |
COPD |
2.31 |
(1.31-4.08) |
0.004 |
1.03 |
(0.55-1.94) |
0.913 |
Heart Failure |
4.09 |
(2.95-5.66) |
<0.001 |
1.42 |
(0.91-2.23) |
0.116 |
Coronary Artery Disease |
2.33 |
(1.67-3.26) |
<0.001 |
0.83 |
(0.52-1.32) |
0.44 |
History of CVA |
12.1 |
(4.28-34.65) |
<0.001 |
8.45 |
(2.52-28.31) |
0.001 |
Treatment |
HSCT |
1.05 |
(0.72-1.53) |
0.773 |
1.04 |
(0.67-1.61) |
0.849 |
Chemotherapy |
0.3 |
(0.07-1.23) |
0.095 |
0.602 |
(0.16-2.22) |
0.447 |
Hospital characteristics (%)
|
|
|
|
|
|
|
Bed size of hospital
(STRATA)
|
|
|
|
|
|
|
Small Reference |
|
|
|
|
|
|
Medium |
1.58 |
(0.94-2.65) |
0.08 |
1.95 |
(1.06-3.59) |
0.03 |
Large |
1.83 |
(1.17-2.87) |
0.008 |
1.7 |
(0.99-2.93) |
0.054 |
Hospital location |
|
|
|
|
|
|
Rural Reference |
|
|
|
|
|
|
Urban |
0.34 |
(0.16-.72) |
0.005 |
0.41 |
(0.13-1.22) |
0.111 |
Hospital teaching status
|
Non-teaching hospital
Reference
|
Teaching hospital |
0.56 |
(0.36-.88) |
0.013 |
0.81 |
(0.44-1.50) |
0.516 |
Region of hospital
|
Northeast Reference
|
Midwest |
0.6 |
(0.39-0.93) |
0.022 |
0.57 |
(0.35-0.93) |
0.024 |
South |
0.86 |
(0.61-1.23) |
0.434 |
0.82 |
(0.57-1.18) |
0.305 |
West |
0.73 |
(0.50-1.08) |
0.119 |
0.85 |
(0.56-1.29) |
0.459 |
Univariate regression analysis revealed an association between
increased mortality and concurrent malnutrition in patients with
acute Leukemia (OR 3.33, 95% CI: 2.49-4.45, p<0.001). Upon
conducting multivariate logistic regression analysis to adjust for
confounding variables, it was determined that malnutrition independently contributes to higher mortality among hospitalized
acute leukemia patients (OR 2.89, 95% CI: 2.11-3.94, p<0.001)
(Table 2).
Likewise, Tables 3 and 4 present the results of univariate and
multivariate analyses for length of stay and total hospitalization
charges. Following adjustment for confounding factors, the multivariate regression analysis indicated a significant increase in both
length of stay (+9.1 Days 95% CI: 7.01-11.19, p<0.001) and total
hospitalization charges (+177994 USD, 95% CI: 122573-2334512,
p<0.001) among acute leukemia patients with concurrent malnutrition.
Table 3: Univariate and multivariate regression analysis of length of hospitalization in acute leukemia patients with and
without malnutrition.
|
Univariate regression
|
Multivariate regression
|
LOS (Days) |
Coefficient |
95% Conf. Interval |
P- Value |
Coefficient |
95% Conf. Interval |
P- Value |
Malnutrition |
12.22 |
(9.93-14.51) |
<0.001 |
9.1 |
(7.01-11.19) |
<0.001 |
Gender (%) |
|
|
|
|
|
|
Male Reference |
|
|
|
|
|
|
Female |
0.6 |
(-0.54-1.75) |
0.303 |
1.1 |
( -0.17-2.38) |
0.09 |
Age |
|
|
|
|
|
|
Mean age |
-0.05 |
(-0.07-(-0.02)) |
<0.001 |
-0.16 |
(-0.25(-0.06)) |
0.001 |
Age distribution (%) |
|
|
|
|
|
|
<18 |
Reference |
|
|
|
|
|
18-35 |
2.97 |
(0.93-5.00) |
0.004 |
0.81 |
(1.15-2.79) |
0.417 |
36-45 |
1.53 |
(-0.65-3.72) |
0.17 |
-1.28 |
(-3.47-0.91) |
0.252 |
46-64 |
1.568 |
(-0.12-3.26) |
0.07 |
-0.94 |
(-2.97-1.09) |
0.365 |
>65 |
-4.29 |
( -6.21-(-2.37)) |
<0.001 |
-2.72 |
(-5.47-.01) |
0.052 |
Race (%) |
|
|
|
|
|
|
White Reference |
|
|
|
|
|
|
Black |
1.07 |
(-0.80-2.94) |
0.262 |
-0.95 |
(-2.69-0.78) |
0.283 |
Hispanic |
3.03 |
(1.30-4.76) |
0.001 |
1.32 |
(0.01-2.63) |
0.047 |
Other |
3.84 |
(0.87-6.82) |
0.011 |
2.3 |
(-0.31-4.97) |
0.084 |
Median household income
national quartile for
patient zip code (%)
|
|
|
|
|
|
|
$1-$49,999 |
Reference |
|
|
|
|
|
$50,000-$64,999 |
-0.53 |
(-2.50-1.43) |
0.595 |
-0.56 |
(-2.45-1.31) |
0.553 |
$65,000-$85,999 |
-1.27 |
(-3.03-.48) |
0.154 |
-1.31 |
(-3.04-.41) |
0.137 |
>$86,000 |
-0.55 |
(-2.38-1.27) |
0.551 |
-0.96 |
(-2.73-0.79) |
0.282 |
Charlson comorbidity index
(%)
|
|
|
|
|
|
|
2 |
Reference |
|
|
|
|
|
3 or more |
1.89 |
(0.63-3.15) |
0.003 |
3.68 |
(2.04-5.3) |
<0.001 |
Insurance provider (%) |
|
|
|
|
|
|
Medicare Reference |
|
|
|
|
|
|
Medicaid |
6 |
(4.21-7.76) |
<0.001 |
4.15 |
(2.05-6.26) |
<0.001 |
Private |
4.42 |
( 3.10-5.73) |
<0.001 |
2.47 |
(0.71-4.23) |
0.006 |
Uninsured |
1.93 |
(-1.38-5.26) |
0.253 |
0.95 |
(-1.65-3.56) |
0.474 |
Comorbidities (%) |
|
|
|
|
|
|
Hypertension |
2.39 |
(1.15-3.64) |
<0.001 |
2.49 |
(1.09-3.89) |
<0.001 |
Diabetes mellitus |
-0.64 |
(-2.31-1.02) |
0.451 |
-2.02 |
(-3.96-(-0.08)) |
0.041 |
Fluid and electrolyte
disorders
|
11.47 |
(10.10-12.85) |
<0.001 |
8.94 |
(7.59-10.28) |
<0.001 |
Chronic kidney disease |
|
|
|
|
|
|
CKD2 |
Reference |
|
|
|
|
|
CKD3 |
-4.77 |
(-7.41-2.13) |
<0.001 |
0.57 |
(-2.31-3.47) |
0.696 |
CKD4 |
-5.77 |
(-15.50-3.96) |
0.245 |
-4.98 |
(-13.23-3.25) |
0.236 |
CKD5 |
-15.31 |
(-16.10-(-14.53) |
<0.001 |
-11.18 |
(-14.85-(-7.51) |
<0.001 |
CKD Unspecified |
-1.8 |
(-4.63-1.02) |
0.212 |
-1.27 |
(-4.21-1.65) |
0.393 |
ESRD |
8.88 |
(-17.78- 35.55) |
0.514 |
-2.56 |
(-12.02-6.90) |
0.596 |
Hyperlipidemia (HLD) |
-2.89 |
(-4.18-(-1.59) |
<0.001 |
-1.28 |
(-2.59-.02) |
0.055 |
Obesity |
2.05 |
(0.33-3.76) |
0.019 |
1.65 |
(-0.13-3.44) |
0.071 |
COPD |
-5.61 |
(-7.92-(-3.30) |
<0.001 |
-3.42 |
(-5.95-(-0.88) |
0.008 |
Heart failure |
-1.14 |
(-3.76-1.48) |
0.394 |
2.05 |
(-0.53-4.63) |
0.12 |
Coronary artery disease |
-4.3 |
(-5.95-(-2.64)) |
<0.001 |
-0.9 |
(-2.42-0.60) |
0.239 |
History of CVA |
17 |
(2.36-31.64) |
0.023 |
15.24 |
(0.82-29.67) |
0.038 |
Treatment |
|
|
|
|
|
|
HSCT |
16.78 |
( 14.66-18.91) |
<0.001 |
13.25 |
(11.23-15.26) |
<0.001 |
Chemotherapy |
0.14 |
(-3.88-4.17) |
0.944 |
1.47 |
(-2.47-5.42) |
0.464 |
Discharge disposition (%)
|
|
|
|
|
|
|
Home Reference |
|
|
|
|
|
|
Home with home health |
4.26 |
(2.75-5.78) |
<0.001 |
3.26 |
(1.86-4.66) |
<0.001 |
Skilled nursing facility
|
-10.1 |
(-12.28-(-7.92)) |
<0.001 |
-6.43 |
(-9.19-(-3.68)) |
<0.001 |
Against medical advice |
-8.76 |
(-13.85-(-3.67)) |
0.001 |
-6.1 |
-11.07(-1.13) |
0.016 |
Hospital characteristics (%)
|
|
|
|
|
|
|
Bed size of hospital
(STRATA)
|
|
|
|
|
|
|
Small Reference |
|
|
|
|
|
|
Medium |
0.66 |
(-2.15-3.47) |
0.646 |
2.2 |
(0.01-4.40) |
0.048 |
Large |
3.24 |
(1.41-5.07) |
0.001 |
3.75 |
(2.11-5.39) |
<0.001 |
Hospital location |
|
|
|
|
|
|
Rural Reference |
|
|
|
|
|
|
Urban |
12 |
(9.46-14.54) |
<0.001 |
-0.22 |
(-3.54-3.09) |
0.894 |
Hospital teaching status
|
|
|
|
|
|
|
Non-teaching hospital
Reference
|
|
|
|
|
|
|
Teaching hospital |
11.67 |
(9.98-13.37) |
<0.001 |
7.25 |
(5.57-8.94) |
<0.001 |
Region of hospital |
|
|
|
|
|
|
Northeast Reference |
|
|
|
|
|
|
Midwest |
-3.48 |
(-6.38-0.57) |
0.019 |
-3.52 |
(-6.05-(-0.99)) |
0.006 |
South |
-4.27 |
( -6.41-(-2.13)) |
<0.001 |
-3.49 |
(-5.31-(-1.68)) |
<0.001 |
West |
-2.79 |
(-5.30-0.29) |
0.029 |
-3.27 |
(-5.37-(-1.16)) |
0.002 |
LOS: Length of Stay; CVA: Cardiovascular Accident; HSCT: Hematopoietic Stem Cell Transplant; COPD: Chronic Obstructive
Pulmonary Disease; ESRD: End Stage Renal Disease.
Table 4: Univariate and multivariate regression analysis of the total cost of hospitalization in acute leukemia patients with
and without malnutrition.
|
Univariate regression
|
Multivariate regression
|
Total charges (USD) |
Coefficient |
95% Conf. Interval |
P-Value |
Coefficient |
95% Conf. Interval |
P-Value |
Malnutrition |
241074 |
(172942-309206) |
<0.001 |
177994 |
(122573-2334152) |
<0.001 |
Gender (%) |
|
|
|
|
|
|
Male Reference |
|
|
|
|
|
|
Female |
-12146 |
(-39614.74-15320) |
0.386 |
-984 |
(-28247-26278) |
0.944 |
Age |
|
|
|
|
|
|
Mean Age |
-2292 |
(-2868-(-1717)) |
<0.001 |
-705 |
(-3137-1726) |
0.57 |
Age distribution (%) |
|
|
|
|
|
|
<18 |
Reference |
|
|
|
|
|
18-35 |
33434 |
(-16545-83413) |
0.19 |
-34574 |
(-82629-13481) |
0.158 |
36-45 |
-30085 |
(-94271-34099) |
0.358 |
-116145 |
(-183158-(-49133)) |
0.001 |
46-64 |
-51340 |
(-95809-(-6872) |
0.024 |
-128959 |
(-180341-(-77577) |
<0.001 |
>65 |
-171022 |
(-213299-(-128744) |
<0.001 |
-174717 |
(-234173-(-1152619)) |
<0.001 |
Race |
(%) |
|
|
|
|
|
White Reference |
|
|
|
|
|
|
Black |
9649 |
(-28568-47866) |
0.621 |
-18239 |
(-55236-18756) |
|
Hispanic |
108506 |
(-66686-150326) |
<0.001 |
50159 |
(14314-86005) |
|
Other |
138313 |
(40912-235715) |
0.005 |
83790 |
(-2571-1701529) |
|
Median household income
national quartile for
patient zip code (%)
|
|
|
|
|
|
|
$1-$49,999 |
Reference |
|
|
|
|
|
$50,000-$64,999 |
2.35 |
(-40057-40062) |
1 |
-556 |
(-38264-371518) |
0.977 |
$65,000-$85,999 |
-4180 |
(-42406-34045) |
0.83 |
-9511 |
(-49152-30129) |
0.638 |
>$86,000 |
39398 |
(-7187-85984) |
0.097 |
17261 |
(-29118-636429) |
0.466 |
Charlson comorbidity index
(%)
|
|
|
|
|
|
|
2 |
Reference |
|
|
|
|
|
3 or more |
31681 |
(1815-61547) |
0.038 |
103058 |
(64436-1416816) |
<0.001 |
Insurance provider (%) |
|
|
|
|
|
|
Medicare Reference |
|
|
|
|
|
|
Medicaid |
176027 |
(136517-215538) |
<0.001 |
56641 |
(11743-1015394) |
0.013 |
Private |
127732 |
(97269-158194) |
<0.001 |
27973 |
(-8198-64145) |
0.13 |
Uninsured |
69214 |
(-11052-149482) |
0.091 |
19842 |
(-45124-84810) |
0.549 |
Comorbidities (%) |
|
|
|
|
|
|
Hypertension |
24341 |
(-6155-54838) |
0.118 |
47040 |
(14334-797454) |
0.005 |
Diabetes mellitus |
-41171 |
(-76190-(-6152)) |
0.021 |
-43588 |
(-84273-(-2903)) |
0.036 |
Fluid and electrolyte
disorders
|
223743 |
(186465-261021) |
<0.001 |
179347 |
(142487-2162072) |
<0.001 |
Chronic kidney disease |
|
|
|
|
|
|
CKD2 |
4306 |
(-267427-276040) |
0.975 |
|
|
|
CKD3 |
-155663 |
(-200539-(-110786)) |
<0.001 |
-24337 |
(-74422-25748) |
0.341 |
CKD4 |
8206 |
(-452915-469329) |
0.972 |
61446 |
(-353672-4765649) |
0.772 |
CKD5 |
-294682 |
(-319034-270329) |
0.001 |
-237375 |
(-322733-(-1520168) |
<0.001 |
CKD Unspecified |
-75804 |
(-138780-12828) |
0.018 |
-45690 |
(-98059-6678) |
0.087 |
ESRD |
310856 |
(-320893-942605) |
0.335 |
63140 |
(-253520-3798008) |
0.696 |
Hyperlipidemia (HLD) |
-110815 |
(-139941-81689) |
<0.001 |
-31877 |
(-62459-(-1295) |
0.041 |
Obesity |
25521 |
(-15341-66383) |
0.221 |
23895 |
(-15904-63694) |
0.239 |
COPD |
-154875 |
(-198346-(-111404)) |
<0.001 |
-82430 |
(-131420-(-33441)) |
0.001 |
Heart Failure |
-20405 |
(-78902-38092) |
0.494 |
76610 |
(12026-141193) |
0.02 |
Coronary artery disease |
-133653 |
(-164490-(-102815)) |
<0.001 |
-31617 |
(-63167-(-68)) |
0.05 |
History of CVA |
568867 |
(84078-1053655) |
0.021 |
564339 |
(91306-103732) |
0.019 |
Treatment |
|
|
|
|
|
|
HSCT |
423121 |
(348684-497558) |
<0.001 |
374343 |
(302562-4461248) |
<0.001 |
Chemotherapy |
-30098 |
(-123753-63557) |
0.529 |
-20048 |
(-106566-66468) |
0.65 |
Discharge disposition (%)
|
|
|
|
|
|
|
Home Reference |
|
|
|
|
|
|
Home with home health |
39155 |
(5639-72670) |
0.022 |
|
|
|
Skilled nursing facility
|
-68168 |
(-121801-(-14536)) |
0.013 |
|
|
|
Against medical advice |
-80256 |
(-177628-17116) |
0.106 |
|
|
|
Hospital characteristics (%)
|
|
|
|
|
|
|
Bed size of hospital
(STRATA)
|
|
|
|
|
|
|
Small Reference |
|
|
|
|
|
|
Medium |
19066 |
(-60306-98438) |
0.638 |
31708 |
(-35338-98755) |
0.354 |
Large |
54120 |
(-1989-106252) |
0.042 |
55556 |
(4381-1067313) |
0.033 |
Hospital location |
|
|
|
|
|
|
Rural Reference |
|
|
|
|
|
|
Urban |
268029 |
(237291-298767) |
<0.001 |
3115 |
(-49891-56122) |
0.908 |
Hospital teaching status
|
|
|
|
|
|
|
Non-teaching hospital |
Reference |
|
|
|
|
|
Teaching hospital |
237696 |
(200644-274748) |
<0.001 |
101844 |
(66207-1374813) |
<0.001 |
Region of hospital |
|
|
|
|
|
|
Northeast |
Reference |
|
|
|
|
|
Midwest |
-112853 |
(-179928-(-45779)) |
0.001 |
-123239 |
(-185143-61335) |
<0.001 |
South |
-122192 |
(-184028-(-60357)) |
<0.001 |
-108294 |
(-165819-50768) |
<0.001 |
West |
10398 |
(-73145-93942) |
0.807 |
-29572 |
(-103717-44571) |
0.434 |
USD: United States Dollar; CVA: Cardiovascular Accident; HSCT: Hematopoietic Stem Cell Transplant; COPD: Chronic Obstructive
Pulmonary Disease; ESRD: End Stage Renal Disease.
Discussion
Our comprehensive study revealed the alarming impact of
malnutrition on hospitalized acute leukemia patients, with a staggering threefold increase in mortality. Additionally, our findings
also highlighted its substantial influence on prolonged hospital
stays and increased overall hospitalization costs. Research by
Deenadayalan et al. shed light on the prevalence of malnutrition
in Diffuse Large B Cell Lymphoma (DLBCL), revealing that 7% of
chemotherapy-admitted patients suffered from concurrent malnutrition [9]. Another study conducted by Park et al. reported
a higher range of 10-20% for the prevalence of malnutrition in
DLBCL patients [10]. In alignment with these insights, our own research indicated a notable 13% prevalence rate of malnutrition
among acute leukemia patients.
In our study, it was found that a higher percentage of Hematopoietic Stem Cell Transplants (HSCT) occurred in malnourished
patients compared to those without malnutrition. According to
Kim et al. HSCT failures are recognized as potential and devastating complications of malnutrition, and malnutrition is also considered a poor prognostic factor for outcomes in HSCT patients
[11]. The increased occurrence of HSCT in our study’s cohort of
malnourished patients is likely due to the fact that HSCT failures
necessitate multiple repeat interventions and procedures within
this patient group. Previous studies have extensively elucidated
the association between malnutrition and adverse outcomes in
leukemia patients undergoing HSCT.
Amiri Khosroshahi et al. conducted a comprehensive singlecenter observational, longitudinal, and prospective study involving 98 adult leukemia patients who had undergone hematopoietic stem cell transplantation [12]. The study focused on evaluating
outcomes such as mortality rates, the occurrence of oral mucositis, infection incidences, and readmission rates. Their findings
suggested that malnutrition correlated with an increased risk of
infections and readmissions; however, it did not show any significant difference in overall survival [12]. In contrast to their conclusions, our own research indicated a higher incidence of mortality
among malnourished patients. It is important to note that while
the aforementioned study had a relatively small cohort size, our
study encompassed a larger group, including both transplant candidates and non-transplant candidates, for better representation.
Our study revealed a striking disparity in the rates of chemotherapy undergone by patients with malnutrition compared to
those without. This discrepancy can be attributed to the formidable toxicity of induction and maintenance therapies, rendering them particularly challenging for individuals grappling with
malnutrition. Not only does malnutrition compromise immune
function, but it also undermines overall physical endurance and
muscle capability, profoundly affecting quality of life. Additionally,
it significantly attenuates the body’s capacity to withstand chemotherapy, heightening susceptibility to treatment-related toxicity and complications while concurrently diminishing survival
prospects.
Van Cutsem et al. conducted an extensive study highlighting
the profound impact of chemotherapy on the immune system.
They noted that when combined with malnutrition, these effects
can drastically reduce a patient’s ability to tolerate the adverse
effects of chemotherapy, ultimately leading to decreased compliance rates and overall survival outcomes [13].
Similarly, Malihi et al. conducted a prospective study to comprehensively assess the alterations in the nutritional status of leukemia patients both prior to and following induction therapy [14].
Their cohort comprised 63 leukemia patients who were subjected
to induction chemotherapy. The findings revealed that 19.4% of
these patients experienced malnutrition before undergoing induction chemotherapy, while after the completion of therapy, this
figure escalated significantly to 76.1% [14]. This data underscores
the rationale behind our observation that individuals with preexisting malnutrition were less inclined to undergo chemotherapy
based on our results.
Several recent studies have delved into the profound impact of
malnutrition on healthcare resource utilization and patient outcomes in various severe health conditions. A prospective study
by Lim et al. meticulously utilized Subjective Global Assessment
to identify malnourished patients, enrolling a substantial cohort
of 818 individuals. The extensive analysis revealed compelling
evidence linking malnutrition to prolonged hospital stays, escalated treatment expenses, and heightened readmission rates
[15]. Furthermore, Inciong et al. conducted an expansive multicountry investigation across Asia due to the pervasive prevalence
of malnutrition within the continent. Their findings underscored
a significant upsurge in total hospitalization costs attributable to
malnutrition-related factors, amounting to an estimated annual
expenditure exceeding 30 billion dollars for additional treatments
[16].
Malnourishment not only increases the likelihood of sickness
and death among hospital patients, but it also contributes to a
substantial increase in healthcare expenses. The prolonged duration of hospitalization and increased expenses could signify the
intricate nature and seriousness of clinical intervention needed
for malnourished patients. This underscores the need for a more
thorough and holistic approach to their treatment, potentially
involving extra resources such as specialized medical knowledge,
personalized care plans, and continuous support services. By implementing a comprehensive screening tool, healthcare providers
can effectively identify at-risk patients and provide them with the
necessary care, ultimately leading to enhanced clinical outcomes
and reduced healthcare costs.
Several constraints need to be recognized when interpreting
the results of this study. Firstly, it’s important to acknowledge that
the analysis was based on retrospective data from the NIS 2019-
2020 database, which relies on the accuracy and completeness
of recorded data. Variations in coding practices across healthcare
facilities could significantly impact these findings. It would be
beneficial for future studies to explore utilizing prospective study
designs to capture detailed clinical information directly, thereby
minimizing variations and potential inaccuracies associated with
databases’ retrospective nature. Additionally, implementing a
more comprehensive sampling approach that includes both hospitalized and outpatient cases should also be considered, as it
may provide a more holistic view of the subject matter.
The absence of malnutrition severity in the NIS data could significantly impact the findings and their real-world relevance. By
solely focusing on hospitalized patients, there is a potential for
bias, as it excludes less severe cases treated on an outpatient basis. Moreover, the study overlooks consideration of various treatment methods that might have a substantial influence on the
analysis of outcomes. Despite its limitations, this study provides
valuable initial insights into the relationship between malnutrition and hospitalized acute leukemia patients. It suggests a need
for additional prospective research to validate and build upon its
findings with more depth and context into different aspects affecting these patients’ nutritional status
Conclusion
This retrospective study analyzed a compelling association
between malnutrition and adverse outcomes in patients hospitalized with acute leukemia. The findings underscore the critical importance of addressing and mitigating malnutrition as an integral
component of comprehensive care for this vulnerable patient population. Beyond serving as a prognostic marker, understanding
the impact of malnutrition on clinical outcomes can inform tailored interventions and strategies aimed at improving the overall
prognosis and quality of life for individuals with acute leukemia.
Further research should delve into the underlying mechanisms
linking malnutrition and poor outcomes, paving the way for targeted therapeutic interventions and enhanced supportive care strategies in the management of acute leukemia patients.
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