Comparing characteristics and outcomes between hospitalized adults on a pea protein or dairy/soy protein formulas: initial findings
Abstract
Enteral formulas are an essential part of nutrition support to prevent or treat malnutrition and minimize hospital length of stay (LOS). Yet not all formulas are tolerated and may leave nutritional needs unmet. Unique pea protein plant-based formulas (PPPBF) are nutritionally complete and have accumulated evidence of good tolerance, but impact on health economic outcomes (HEO) is largely unknown. Aim: To examine differences in patient and clinical characteristics and HEOs of hospitalized adults using PPPBF versus dairy and/or soy protein (DSP) formulas to inform further research. Materials & methods: Retrospective comparative cohort study examined real-world data (Premier Healthcare Database) from adults (≥18 year) admitted to US hospitals who were prescribed a formula between 1 January 2020 and 30 September 2023. Patient and clinical characteristics and HEOs were compared between the PPPBF and DSP groups by unadjusted descriptive analysis. Results: Preliminary analyses were conducted on inpatients (n = 65,338 DSP; n = 243 PPPBF) from 60 US hospitals. PPPBF (versus DSP) group was younger (mean [SD] 63.6 [17.0] vs 66.7 [17.3] years; p = 0.006) and had a higher diagnosis rate for malnutrition, weight loss, food allergies, irritable bowel syndrome and/or inflammatory bowel disease on admission. Overall, formula intake was primarily oral but higher in PPPBF versus DSP (100 vs 78.3%, p < 0.001). Charlson Comorbidity Index indicated PPPBF (versus DSP) was sicker (median 4.0 vs 3.0; p < 0.001). Yet PPPBF group had shorter LOS (by ∼2 days; p < 0.001) and lower mortality rate by discharge (5.8% vs 11.5%; p = 0.005) without significant difference in 90-day readmission/outpatient visit rates after discharge (unadjusted comparisons). Conclusion: Preliminary evidence in hospitalized adults observed a shorter LOS in PPPBF users, despite higher baseline acuity and without significant difference in readmission/outpatient visit rates compared (unadjusted) to DSP users. Adjusted analyses and further research are needed.
Plain language summary: A comparison between two groups of hospitalized adults based on the formula they used: initial findings
What was the aim of this research?
To identify and compare preliminary clinical and healthcare resource use differences between hospitalized adults who consumed pea protein plant-based formulas (PPPBF) or dairy and/or soy protein (DSP) formulas for further study.
How was the research carried out?
Real-world deidentified data previously collected by Premier Healthcare Database included adults in US hospitals who were prescribed a formula over 3-year study period (2020–2023). Study groups were assigned based on formula used: PPPBF or DSP. Patient and clinical characteristics and health economic outcomes, like hospital length of stay, were compared between groups.
What were the results?
Preliminary analyses were performed on large dataset from 60 US hospitals (DSP = 65,338; PPPBF = 243). The PPPBF group was younger and sicker than DSP group based on measures related to risk of death, nutritional status and gastrointestinal function. Initial results showed the PPPBF group had a shorter length of stay, lower rate of death in the hospital and similar percentage of hospital readmissions or outpatient visits after discharge compared with the DSP group. Further testing to confirm these preliminary findings is underway.
What do the results of the study mean?
The use of PPPBF, as an alternative to DSP formulas, to supplement nutritional intake in the hospital may have benefits for the patient and healthcare system despite greater illness severity and nutritional risk as observed in this study. In this preliminary study, those in the PPPBF group left the hospital sooner and had comparable rates of readmissions or outpatient visits compared with DSP group. More testing of these findings is underway.
Background
Enteral nutrition (EN), provided by tube or oral feedings, is a cornerstone of nutritional support for hospitalized patients who cannot meet their nutritional needs [1]. Carefully planned and monitored enteral feeding plans are essential to minimize malnutrition risk and optimize patient outcomes [2,3]. In the hospital setting, malnutrition elevates the risk of infections, pressure injuries, prolonged mechanical ventilation, anemia, compromised cardiac and respiratory functions, leading to increased length of hospital stay and mortality [4–8]. Thus, the availability of an enteral formula to meet nutritional requirements while optimizing tolerance and metabolic support is critical.
The high prevalence of malnutrition following hospitalization (20–45%) highlights the critical need for timely interventions by a registered dietitian to implement nutrition risk screenings, assessments and interventions during and after hospital stays [9]. EN initiated early, ideally within 24–48 h of admission, improves clinical outcomes, especially in critically ill patients [10,11]. Selection of an appropriate enteral formula, however, depends on multiple factors, including individual needs and preferences, underlying medical conditions and gastrointestinal (GI) tolerance [12]. Since the 1960s, dairy protein-based formulas (derived from cow’s milk proteins such as casein and/or whey) have been the standard of care for EN patients as they provide a complete protein source. Soy protein-based formulas later became the first widely used plant-based alternative, also offering a complete source of protein. Initially, these formulations were deliberately fiber-free or “low residue” in an attempt to minimize fecal output; despite updated clinical practice guidelines invalidating this, fiber-free formulas remain in use. Given the prevalence of food allergies and growing evidence of the potential negative environmental effects from heavy reliance on dairy and soy, yellow pea protein has emerged as a possible alternative. Recent studies have investigated a unique fiber-containing, yellow pea protein plant-based formula (PPPBF) and its effects on gastric emptying, GI tolerance, prebiotic activity, production of beneficial gut metabolites and patient and caregiver reported outcomes. This research suggests these formulations may improve GI tolerance, support patient adherence, enhance prebiotic activity and promote moderate gastric emptying (relative to casein- or whey-based formulas), while also providing dietary fiber and a low allergen profile – attributes that address issues historically associated with traditional EN formulas [13–15]. Tailoring nutrition therapy to individual patient needs and preferences is replacing the ‘one-size-fits-all’ approach to EN [16].
There is an increasing body of evidence of healthcare cost benefits related to the timely achievement of an individual’s EN goals. Patients who reach their EN goals may be transferred out of the intensive care unit setting and/or discharged from the hospital sooner resulting in lower costs for the healthcare system [17,18]. However, symptoms of GI intolerance can impede the successful delivery of the enteral formula, delay the patient’s attainment of the goal infusion rate/nutritional requirements, and postpone hospital discharge. The prevalence of enteral feeding intolerance (EFI) is substantial in the hospital setting, estimated to occur in 30.5–65.7% of hospitalized patients (more adults than pediatrics) [19,20]. GI related medications are used to help manage EFI symptoms, which add additional costs. Thus, efforts to find EN formulas to aid in GI tolerance have the potential to improve quality of life as well as minimize healthcare resource utilization (HCRU).
For decades, EN studies centered largely on dairy and soy protein-based formulas, given that these were essentially the main options available (apart from very specialized or elemental amino acid formulas). A gap, therefore, exists in research focused on pea protein plant-based enteral formula use. The primary objective of this study was to use real-world data to assess the differences in health economic outcomes between hospitalized adults who received a unique pea protein plant-based formula (PPPBF, Kate Farms™) and those who received a formula made with dairy and/or soy protein (DSP) formulas. The secondary objective was to compare patient and clinical characteristics and other HEOs between the PPPBF and DSP groups to identify differences to investigate in further analyses. This short report presents initial descriptive, unadjusted findings to inform further research.
Materials & methods
Data source & study design
This retrospective comparative cohort study was conducted using real-world data from the Premier Healthcare Database (PHD) [21]. The PHD includes hospital administrative data from healthcare systems across the US. In accordance with the HIPAA Privacy Rule, disclosed data from the PHD are considered deidentified per US Code of Federal Regulations (CFR) 45 Part 164.514(b)(1) through the ‘Expert Determination’ method. The study was not considered human subjects research and it was determined to be exempt from IRB oversight by a central IRB under US CFR 45 Part 46.106(d)(4). Study inclusion criteria were defined as adult patients (≥18 years) who had a hospitalization in one of the reporting sites and evidence of use of any brand of EN formula between 1 January 2020 and 30 September 2023. For each patient, the index visit was defined as the first hospitalization within the time period that also included an EN formula order. Study participants were assigned to one of two groups based on their EN formula order. The PPPBF group sought to include participants who received any Kate Farms brand of formula (Standard 1.0, Standard 1.4, Peptide 1.0, Peptide 1.5).The dairy and/or soy protein (DSP) formula group sought to include participants who received ‘all other brands’ of formulas with these protein sources, with or without fiber, and excluded specialized DSP formulas that didn't have a comparable formula in the PPPBF group (e.g., immunomodulating or reduced calorie).
Data & study outcomes
The primary outcome was length of stay (LOS) of the index visit, as this is a measure of HCRU and is influenced by the successful tolerance of the EN/dietary regimen during the admission. This study compared two groups, both of which received a nutritional formula during hospitalization, rather than comparing a treatment group with a non-treatment group. Additionally, the collection of data on the percentage of inpatient days involving any formula use helped to minimize immortal time bias. Secondary outcomes included in-hospital deaths by index visit discharge and clinical end points measured during the 90-day post-index visit follow-up (all-cause hospital readmissions, all-cause outpatient medical visits). Patient and clinical characteristics (present on admission and/or reported during index visit) included demographics and data such as enteral formula use, CCI score/comorbidities, GI-related medication use, GI intolerance symptoms and nutritional status (based on weight loss/malnutrition diagnosis). The CCI score was calculated using a combination of the original and updated methods [22,23].
Statistical analyses
Descriptive statistics are presented as mean (standard deviation [SD]) and median (interquartile range [IQR]) for continuous variables or as count (%) for categorical variables. Unadjusted statistical comparisons between groups for categorical or binary variables were performed using Pearson’s Chi-squared test or Fisher’s exact test when expected cell counts were small. Welch's two-sample t-test was performed for age, and the Wilcoxon rank sum test was performed for count variables, as they are typically not normally distributed. Significance was determined at a p-value < 0.05. Statistical analyses were performed using R version 4.4.1.
Results
Demographics at index visit admission (Table 1)
During the study period, 65,581 patients receiving EN formulas were identified (65,338 DSP, 243 PPPBF) across 60 US hospitals. The mean (SD) age was 66.7 (17.3) and 63.6 (17.0) years for DSP and PPPBF groups, respectively (p = 0.006). Overall, the study population was mostly male (51.7%), white (72.4%), non-Hispanic or Latino (96.8%), and had Medicare as their primary insurance payer (64.5%).
| DSP (N = 65,338) | PPPBF (N = 243) | p-value | |
|---|---|---|---|
| Sex | 0.106† | ||
| Female | 31,561 (48.3%) | 130 (53.5%) | |
| Male | 33,777 (51.7%) | 113 (46.5%) | |
| Age (years) | |||
| Mean (±SD) | 66.7 ± 17.3 | 63.6 ± 17.0 | 0.006‡ |
| Median (IQR) | 69.0 (57.0, 79.0) | 66.0 (53.0, 76.0) | |
| Age categorized (years) | 0.154† | ||
| 18–39 | 5860 (9.0%) | 28 (11.5%) | |
| 40–64 | 20,065 (30.7%) | 82 (33.7%) | |
| 65+ | 39,413 (60.3%) | 133 (54.7%) | |
| Race | <0.001§ | ||
| White | 47,304 (72.4%) | 157 (64.6%) | |
| Asian | 1224 (1.9%) | 4 (1.7%) | |
| African–American | 12,537 (19.2%) | 72 (29.6%) | |
| Other | 4273 (6.5%) | 10 (4.1%) | |
| Ethnicity | 0.714§ | ||
| Hispanic or Latino | 2108 (3.2%) | 6 (2.5%) | |
| Not Hispanic or Latino | 63,230 (96.8%) | 237 (97.5%) | |
| Discharge status | <0.001† | ||
| Home/health | 32,182 (49.3%) | 170 (70.0%) | |
| SNF, ICF or long-term care | 20,029 (30.7%) | 46 (18.9%) | |
| Other | 4533 (6.9%) | 11 (4.5%) | |
| Transferred to acute care | 1075 (1.6%) | 2 (0.8) | |
†
Pearson’s Chi-squared test.
‡
Welch's two sample t-test.
§
Fisher’s exact test.
DSP: Dairy and/or soy protein formula group; ICF: Intermediate care facility; IQR: Interquartile range; PPPBF: Pea protein plant-based formula group; SD: Standard deviation; SNF: Skilled nursing facility.
Clinical characteristics during index visit (unless specified) (Table 2)
Of the 243 patients identified and grouped into the PPPBF group, 100% of them had used Standard 1.4 which was also a fiber-containing formula. Table 2 presents unadjusted comparisons of clinical characteristics, including measures of medical acuity, between formula groups. The median CCI score was higher in the PPPBF group (p < 0.001), with 20.6% having a score of 9 or above (highest category of mortality risk), compared with 8.7% in the DSP group. Higher rates of ‘Any Malignancy’ and ‘Metastatic solid tumor’ diagnoses in the PPPBF group (than the DSP group) were also noted. Further, upon admission to the index visit, the PPPBF group was more likely to present with a diagnosis of malnutrition, weight loss, food allergies, irritable bowel syndrome and/or inflammatory bowel disease (all p < 0.05) than the DSP group. The median percentage of inpatient days involving formula use, relative to the entire hospital stay, was similar between the formula groups (45.5% DSP vs 42.9% PPPBF, p = 0.928). Most of the formula intake for both groups was via ONS (versus tube feedings) but was statistically higher in the PPPBF group (243 [100%] PPPBF; 51,564 [78.3% DSP]; p < 0.001).
| DSP (N = 65,338) | PPPBF (N = 243) | p-value | |
|---|---|---|---|
| Percentage of inpatient days with any formula use | |||
| Mean (±SD) | 47.7% ± 27.7% | 47.9% ± 27.8% | |
| Median (IQR) | 45.5% (25.0, 66.7%) | 42.9% (25.0, 66.7%) | 0.928† |
| Enteral intake, n (%) | |||
| Oral feedings | 51,164 (78.3%) | 243 (100%) | <0.001‡ |
| Tube feedings | 14,174 (21.7%) | 0 (0%) | <0.001‡ |
| CCI score | |||
| Mean (±SD) | 4.0 ± 3.1 | 4.9 ± 3.8 | |
| Median (IQR) | 3.0 (2.0, 6.0) | 4.0 (2.0, 8.0) | <0.001† |
| CCI score (categorized), n (%) | <0.001‡ | ||
| 0 | 7935 (12.1%) | 24 (9.9%) | |
| 1–4 | 33,102 (50.7%) | 111 (45.7%) | |
| 5–8 | 18,624 (28.5%) | 58 (23.9%) | |
| 9+ | 5677 (8.7%) | 50 (20.6%) | |
| CCI comorbidities, n (%) | |||
| Myocardial infarction | 8780 (13.4%) | 25 (10.3%) | 0.151‡ |
| Congestive heart failure | 20,050 (30.7%) | 53 (21.8%) | 0.003‡ |
| Peripheral vascular disease | 6834 (10.5%) | 32 (13.2%) | 0.169‡ |
| Cerebrovascular disease | 10,564 (16.2%) | 27 (11.1%) | 0.032‡ |
| Dementia | 9308 (14.2%) | 14 (5.8%) | <0.001§ |
| Chronic pulmonary disease | 22,089 (33.8%) | 74 (30.5%) | 0.270‡ |
| Rheumatic disease | 2409 (3.7%) | 10 (4.1%) | 0.731§ |
| Peptic ulcer disease | 2203 (3.4%) | 6 (2.5%) | 0.591§ |
| Diabetes with chronic complications | 13,457 (20.6%) | 45 (18.5%) | 0.424‡ |
| Diabetes without chronic complications | 7814 (12.0%) | 32 (13.2%) | 0.562‡ |
| Hemiplegia or paraplegia | 3985 (6.1%) | 7 (2.9%) | 0.031§ |
| Moderate or severe renal disease | 32,118 (49.2%) | 106 (43.6%) | 0.085‡ |
| Any malignancy | 9932 (15.2%) | 93 (38.3%) | <0.001‡ |
| Moderate or severe liver disease | 5114 (7.8%) | 19 (7.8%) | >0.999§ |
| Mild liver disease | 3337 (5.1%) | 18 (7.4%) | 0.108§ |
| Metastatic solid tumor | 4699 (7.2%) | 55 (22.6%) | <0.001‡ |
| HIV disease | 439 (0.7%) | 3 (1.2%) | 0.226§ |
| Other conditions, n (%) | |||
| Food allergies | 1153 (1.8%) | 23 (9.5%) | <0.001§ |
| Irritable bowel syndrome | 996 (1.5%) | 8 (3.3%) | 0.035§ |
| Inflammatory bowel disease (UC, Crohn’s disease) | 2327 (3.6%) | 16 (6.6%) | 0.022§ |
| GI-related medications, n (%) | |||
| Antidiarrheals | 3939 (6.0%) | 24 (9.9%) | 0.012‡ |
| Antiemetics | 36,266 (55.5%) | 156 (64.2%) | 0.006‡ |
| H2 antagonists | 19,015 (29.1%) | 38 (15.6%) | <0.001‡ |
| Proton pump inhibitors | 33,584 (51.4%) | 123 (50.6%) | 0.807‡ |
| Laxatives | 42,658 (65.3%) | 138 (56.8%) | 0.005‡ |
| Prokinetic | 7032 (10.8%) | 26 (10.7%) | 0.975‡ |
| Present on admission: GI intolerance, n (%) | |||
| Report of ≥1 GI intolerance symptoms | 7654 (11.7%) | 49 (20.2%) | <0.001‡ |
| Flatulence | 76 (0.1%) | 0 (0%) | >0.999§ |
| Abdominal pain | 496 (0.8%) | 5 (2%) | 0.040§ |
| Constipation | 5208 (8.0%) | 28 (11.5%) | 0.041‡ |
| Diarrhea | 1419 (2.2%) | 14 (5.8%) | 0.001§ |
| Nausea and vomiting | 1102 (1.7%) | 11 (4.5%) | 0.003§ |
| Reported during index visit: GI intolerance, n (%) | |||
| Report of ≥1 GI intolerance symptoms | 11,831 (18.1%) | 64 (26.3%) | <0.001‡ |
| Flatulence | 161 (0.2%) | 1 (0%) | 0.452§ |
| Abdominal pain | 661 (1.0%) | 6 (2.5%) | 0.039§ |
| Constipation | 7995 (12.2%) | 41 (16.9%) | 0.028‡ |
| Diarrhea | 2652 (4.1%) | 14 (5.8%) | 0.189§ |
| Nausea and vomiting | 1657 (2.5%) | 16 (6.6%) | <0.001§ |
| Present on admission: nutritional status, n (%) | |||
| Malnutrition | 12,885 (19.7%) | 62 (25.5%) | 0.024‡ |
| Weight loss | 3621 (5.5%) | 22 (9.1%) | 0.017‡ |
| Malnutrition and weight loss | 14,361 (22.0%) | 69 (28.4%) | 0.016‡ |
| Reported during index visit: nutritional status, n (%) | |||
| Malnutrition | 14,218 (21.8%) | 63 (25.9%) | 0.116‡ |
| Weight loss | 3712 (5.7%) | 22 (9.1%) | 0.024‡ |
| Malnutrition and weight loss | 15,696 (24.0%) | 70 (28.8%) | 0.082‡ |
†
Wilcoxon rank sum test.
‡
Pearson’s Chi-squared test.
§
Fisher’s exact test.
CCI: Charlson Comorbidity Index; DSP: Dairy and/or soy protein formula group; GI: Gastrointestinal; PPPBF: Pea protein plant-based formula group; IQR: Interquartile range; SD: Standard deviation; UC: Ulcerative colitis.
Formula group differences were also found in GI-related issues and nutritional status (see Table 2). Patients in PPPBF group (compared with DSP group) were more likely to report one or more GI intolerance symptom (flatulence, abdominal pain, constipation, diarrhea and/or nausea/vomiting) upon admission to the hospital index visit (present on admission) and during the index visit (both p < 0.001). GI medication use varied between groups as well. Specifically, patients in the PPPBF group (versus DSP group) were more likely to receive antidiarrheals (9.9% vs 6%) and antiemetics (64.2% vs 55.5%), and less likely to receive H2 antagonists (15.6% vs 29.1%) or laxatives (56.8% vs 65.3%, all p < 0.05). Last, the occurrence of malnutrition/weight loss (a combined measure) was higher overall in the PPPBF group, but a larger absolute increase in the percentage of affected individuals was observed in the DSP group during the index visit.
Study outcomes (Table 3)
Outcomes by index visit discharge (unadjusted comparisons)
Preliminary analyses indicated patients in the PPPBF group had a LOS that was approximately 2 days shorter than the DSP group (median [IQR] 6.0 [4–10] vs 8.0 [5–15] days, p < 0.001; Table 3). The percentage of in-hospital deaths by index visit discharge was also lower in the PPPBF group compared (unadjusted) to the DSP group (5.8% vs 11.5%, p = 0.005; Table 3). The initial comparisons between formula groups showed those in the PPPBF group were more likely to be discharged to home or home health services than the DSP group, whereas those in the DSP group were more likely to be discharged to a skilled nursing facility or long-term care (see Table 1).
| DSP (N = 65,338) | PPPBF (N = 243) | p-value | |
|---|---|---|---|
| Outcomes by index visit discharge | |||
| Length of stay for index visit (days) | |||
| Mean (±SD) | 11.9 ± 11.2 | 8.7 ± 8.7 | |
| Median (IQR) | 8.0 (5.0, 15.0) | 6.0 (4.0, 10.0) | <0.001† |
| Deaths, n (%) | 7519 (11.5%) | 14 (5.8%) | 0.005‡ |
| Clinical end points during 90-day post-index visit follow-up | |||
| All-cause hospital readmission, n (%) | 14,212 (21.8%) | 52 (21.4%) | 0.894‡ |
| All-cause outpatient medical visits, n (%) | 19,407 (29.7%) | 81 (33.3%) | 0.216‡ |
†
Wilcoxon rank sum test.
‡
Pearson’s Chi-squared test.
DSP: Dairy and/or soy protein formula group; IQR: Interquartile range; PPPBF: Pea protein plant-based formula group; SD: Standard deviation.
Clinical end points during 90-day post-index visit follow-up (unadjusted comparisons)
In these preliminary analyses, no statistical differences were found between the DSP and PPPBF groups in the percentage of all-cause inpatient readmissions (21.8% vs 21.4%, p = 0.894) or all-cause outpatient medical visits (29.7% vs 33.3%, p = 0.216) within 90-days of hospital discharge, respectively (see Table 3).
Discussion
Adult inpatients receiving a unique pea protein plant-based formula had a 2-day shorter LOS despite being more ill when compared (unadjusted) to a group of adult inpatients using dairy and/or soy protein formulas in our preliminary study analyses. Despite a higher CCI score, the PPPBF group also had a lower percentage of deaths (in unadjusted comparisons) than the DSP group during index visit and similar percentages of all-cause inpatient readmissions and all-cause outpatient medical visits within 90 days of hospital discharge. This study was an initial step in the research of the users and usage of PPPBF versus DSP formulas in hospitalized adults and associated HEOs. These preliminary findings identified, described and quantified unadjusted differences between groups for further study.
Hospital LOS and readmissions are vital qualitative checkpoints for healthcare systems, including the Hospital Readmission Reduction Program, which penalizes hospitals with high rates of readmissions for certain conditions within 30 days. Facilities can benchmark their performance against others using this metric and share it with the public [24]. Registered dietitians can help to optimize hospital performance metrics by the early identification, treatment and as possible, prevention of malnutrition. Studies have documented the benefits of EN support on improving nutritional status as well as helping to reduce LOS, readmission rates and reduce healthcare costs [25–28].
In the literature, a higher CCI score has been associated with a longer inpatient LOS in adults [29]. In this study, the PPPBF (versus DSP) group was observed to have a higher medical acuity based on a higher median CCI score and a higher prevalence of individuals reporting ≥1 GI intolerance symptom, diagnosis rates of malnutrition, weight loss, irritable bowel syndrome, inflammatory bowel disease and food allergies, and serious comorbidities like ‘malignancy’ and ‘metastatic solid tumor’. Yet this higher acuity in the PPPBF group was not accompanied by a higher percentage of in-hospital deaths, longer LOS or higher hospital readmission rates/outpatient medical visits when compared with the DSP group in preliminary analyses (descriptive, unadjusted comparisons). These outcome differences and potential economic benefits observed between formula groups in this study are noteworthy but should be interpreted with caution until the contribution of the baseline differences identified in this study are tested at the same time. Further analysis is in development.
The role of EN support has been undervalued in the treatment of both acute and chronically ill patients, even though global research has shown that malnutrition and nutrient deficiencies worsen conditions and lead to higher healthcare costs. When intake by mouth is tolerated, the use of oral nutrition supplements (ONS) can avoid the cost and burden of feeding tubes by the patient and healthcare system, and have demonstrated clinical and cost benefits by extending lives of malnourished older hospitalized patients at a low additional cost of $524 per life-year saved in the United States [30]. Additionally, ONS use among hospitalized Medicare patients aged 65 and older is associated with better outcomes and reduced healthcare expenses [31]. Yet in the outpatient/home setting in the United States, ONSs are not reimbursed or covered by the majority of government or private medical insurance policies despite demonstrated clinical and economic benefits [30,31]. EN support, and as possible ONS, are relevant options for providers looking for affordable, evidence-based nutrition strategies [31]. Enteral formula intake as ONS was higher in the PPPBF (versus DSP) group (Table 2), however, it was the primary route of intake for both groups. In the inpatient setting, insurance covers nutritional formula regardless of delivery route, so patient payer types were not relevant to formula ordering, recommendations or prescribing in this study.
Similar to other retrospective studies using hospital administrative databases, our study had some known and suspected limitations. For example, our dataset may be subject to hospital reporting practices, with outliers, missing or incomplete data (e.g., items not billable or reimbursable) and lack clinical details that could impact the characteristics and outcomes studied (e.g., frequency and involvement of registered dietitians, EN used prior to admission, readmissions/outpatient medical visits outside of the hospital system’s database parameters). Our preliminary results focused on outcome differences in the PPPBF versus DSP groups, but future research may perform sensitivity analysis to explore outcome differences in PPPBF versus DSP groups across different formulas (e.g., standard, peptide), age groups, and other relevant subgroups. The PPPBF group is noted to have a smaller and more heterogenous sample than the DSP group but this investigation was the first to evaluate a fiber-containing yellow PPPBF in hospitalized adults, so there was no scientific rationale to restrict analysis of the PPPBF cohort by geographic location; accordingly, the statistical plan included all available PPPBF data across sites. In parallel, strict data governance policies and safe data handling requirements prevented excluding data based on geography or hospital system, ensuring compliance while maximizing the use of all collected data. Finally, because hospital dietetic practices aim to follow uniform, evidence-based nutrition guidelines (which define criteria for initiating oral or tube feedings), one can expect minimal differences in clinical practice patterns between sites and across formula groups, further supporting the decision to analyze an unrestricted, comprehensive dataset.
The descriptive, unadjusted analyses reported in this initial study identified differences in patient characteristics between the PPPBF and DSP formula groups, but these characteristics were not adjusted for in the outcome comparisons. This is an important but expected limitation to these early study results. Further, smaller sample size in one arm may yield lower power in the statistical tests. Future studies involving adjusted or matched analysis will be able to address the difference in patient characteristics between the two groups, and a larger PPPBF sample may improve the power of the tests.
These preliminary study findings help fill a gap in this area of literature, inform further research and develop deeper understanding. These findings are intended to inform our next study which will test group differences identified for an impact on study outcomes and, as appropriate, be controlled for in adjusted analyses (for adjusted exponentiated coefficients, 95% confidence intervals). From this study, we also identified additional data points that would be useful for future prospectively designed studies and those which can help to further control for potential immortal time bias.
Conclusion
This descriptive study provides preliminary results that begin to address an important gap in the literature by highlighting demographic and clinical differences between adults who used a PPPBF as an oral supplement during a hospitalization compared with those who used a DSP formula as an oral supplement or via tube feeding. Additionally, these preliminary results provide a new insight into potential HCRU of PPPBF, used as an ONS, in hospitalized adults requiring nutrition support. Unadjusted comparisons between formula groups indicated that the PPPBF group had a 2-day shorter LOS than the DSP group, despite the PPPBF group presenting with higher indices of medical acuity. This unexpected finding is counterintuitive, given that more acute patients generally have longer hospital stays and has prompted additional research questions. Further research is underway.
Summary points
•
Enteral nutrition can help minimize malnutrition risk and optimize patient outcomes in hospitalized patients.
•
Standard of care includes dairy protein-based formulas and more recently soy protein-based formulas for plant-based alternatives, but these dairy and/or soy protein (DSP) formulas lack universal tolerance.
•
Pea protein plant-based formulas (PPPBFs) are nutritionally complete and accumulating evidence of good tolerance, but impact on health economic outcomes is largely unknown.
•
This retrospective comparative cohort study examined 2020–2023 adult inpatient data (deidentified) from Premier Healthcare Database who were prescribed an enteral formula.
•
A total of 65,581 hospitalized adults prescribed an enteral formula were identified across 60 US hospitals and divided into two study groups based on formula: 243 in PPPBF group and 65,338 in DSP formula group.
•
PPPBF group was younger and sicker than DSP group based on higher diagnosis rates of malnutrition, weight loss, food allergies, irritable bowel syndrome and/or inflammatory bowel disease, reported gastrointestinal intolerance symptoms and comorbidity index score.
•
Yet the PPPBF group overall spent approximately 2 days fewer in hospital and had a lower mortality rate by discharge without a higher percentage of hospital readmissions or outpatient medical visits after discharge.
•
These preliminary study findings suggest hospitalized adults using formula made with a unique yellow pea protein may use less healthcare resources than those using formulas made with dairy and/or soy protein.
Author contributions
V Millovich was responsible for substantial contributions to the design of this work, interpretation of data and writing/editing of the manuscript. IO Shingara was responsible for the interpretation of data, and writing/editing of the manuscript. GS Lopes was responsible for the analysis/interpretation of data, and critical reviewing/editing of the manuscript. M Tyagi was responsible for the analysis of data, and critical review of the manuscript. Z Cao was responsible for contributions to design of this work, analysis/interpretation of data, and critical reviewing/editing of the manuscript. CJ Valentine was responsible for substantial contributions to the design of this work, interpretation of data, and writing/editing of the manuscript. All authors are responsible for the final approval of the manuscript to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Acknowledgments
The authors acknowledge Seth Emont PhD for his contributions to the initial design of this study.
Financial disclosure
Funding to conduct the study was provided by Kate Farms Inc (CA, USA). The publication of the study results was not contingent on the Sponsor's approval or censorship of the manuscript.
Competing interests disclosure
V Millovich, IO Shingara, CJ Valentine – salaried employee of Kate Farms Inc.; shareholder. IO Shingara received consultant and speaker fees (unbranded work) from Mead Johnson Nutrition (MJN) and was on the MJN Speakers Bureau. GS Lopes, Z Cao and M Tyagi are employees of Premier, Inc., and Premier, Inc. received payment from Kate Farms to conduct this research. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.
Writing disclosure
No writing assistance was used in the review, writing, or editing of this manuscript.
Ethical conduct of research
In accordance with the HIPAA Privacy Rule, disclosed data from the Premier Healthcare Database (PHD) are considered de-identified per US Code of Federal Regulations (CFR) 45 Part 164.514(b)(1) through the ‘Expert Determination’ method. The study was not considered human subjects research and it was determined to be exempt from IRB oversight by a central IRB under US CFR 45 Part 46.106(d)(4).
Data transparency statement
This manuscript reports the results of a real-world evidence study. The protocol was not publicly registered, and the raw data are not available due to contractual restrictions. A predefined analysis plan is available upon request.
Open access
This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
References
Papers of special note have been highlighted as: • of interest
1.
Lochs H, Allison SP, Meier R et al. Introductory to the ESPEN guidelines on enteral nutrition: terminology, definitions and general topics. Clin. Nutrition 25, 180–186 (2006).
2.
VanBlarcom A, McCoy M. New nutrition guidelines: promoting enteral nutrition via a nutrition bundle. Crit. Care Nurse 38(3), 46–52 (2018).
3.
Alyumni RA, Aldubayan K, Alsoqeah FF et al. Registered dietitians' enteral feeding practices, obstacles, and needs during the management of critically ill hospitalized patients in Riyadh, Saudi Arabia: a qualitative study. Int. J. Health Sci. (Qassim) 17(5), 5–14 (2023).
4.
Bansal N, Alharbi A, Shah M et al. Impact of malnutrition on the outcomes in patients admitted with heart failure. J. Clin. Med. 13(14), 4215 (2024).
5.
Barker LA, Gout BS, Crowe TC. Hospital malnutrition: prevalence, identification and impact on patients and the healthcare system. Int. J. Environ. Res. Public Health 8(2), 514–527 (2011).
6.
Huo X, Meiyin W, Gao D et al. Geriatric nutrition risk index in the prediction of all-cause and cardiovascular mortality in elderly hypertensive population: NHANES 1999–2016. Front. Cardiovasc. Med. 10, 1203130 (2023).
7.
Ouaijan K, Hwalla N, Kandala N et al. Analysis of predictors of malnutrition in adult hospitalized patients: social determinants and food security. Front. Nutr. 10, 1149579 (2023).
• Paper presents findings of a cross-sectional observational multicenter study of the impact of social and economic characteristics and food security on malnutrition risk in hospitalized adults.
8.
Tignanelli CJ, Bukowiec JC. Hospital based nutrition support: a review of the latest evidence. J. Clin. Nutr. Diet. 3(3), 22 (2017).
9.
Ingstad K, Uhrenfeldt L, Kymre IG et al. Effectiveness of individualised nutritional care plans to reduce malnutrition during hospitalisation and up to 3 months post-discharge: a systematic scoping review. BMJ Open. 10(11), e040439 (2020).
• Paper presents a scoping review of studies that evaluate the effectiveness of individualized nutritional care plans to reduce malnutrition in the hospital and for 3 months after hospital discharge. Based on limited findings, authors conclude that a systematic review using longer follow up period is needed.
10.
Koontalay A, Suksatan W, Teranuch A. Early enteral nutrition met calories goals led by nurse on improve clinical outcome: a systematic scoping review. Iran J. Nurs. Midwifery Res. 26(5), 392–398 (2021).
11.
Preiser J, Arabi YM, Berger MM et al. A guide to enteral nutrition in intensive care units: 10 expert tips for the daily practice. Crit. Care 25(1), 424 (2021).
12.
Owens C, Fang JC. Decisions to be made when initiating enteral nutrition. Gastrointest. Endosc. Clin. N. Am. 17(4), 687–702 (2007).
13.
Cohen S, Ramierez A, Millovich V. Patient-reported outcomes indicate plant-based enteral formula improves nutrition and gastrointestinal symptoms. JPEN J. Parenter. Enteral Nutr. 44(3), 275 (2020).
14.
Cohen S, Millovich V, Newman D et al. Patient-reported outcomes on gastrointestinal tolerance and adherence to a pea protein plant-based enteral formula in children and adults. Front. Nutr. 12, 1–8 (2025).
15.
Akçay K, Suluhan D, Kesik G et al. Nursing practices in enteral nutrition. Clin. Sci. Nutr. 2(1), 1–14 (2020).
16.
Wischmeyer PE. Tailoring nutrition therapy to illness and recovery. Crit. Care 21(Suppl. 3), 15–25 (2017).
17.
Kano K, Yamamoto R, Yoshida M et al. Strategies to maximize the benefits of evidence-based enteral nutrition: a narrative review. Nutrients 17(5), 845 (2025).
• Paper presents a narrative review of recent literature focused on the enteral nutrition practices that optimize its effectiveness in critically ill adults.
18.
Yu A, Xie Y, Zhong M et al. Comparison of the initiation time of enteral nutrition for critically ill patients: at admission vs. 24 to 48 hours after admission. Emerg. Med. Int. 2021, 3047732 (2021).
19.
Eveleens RD, Joosten KFM, De Koning BAE et al. Definitions, predictors and outcomes of feeding intolerance in critically ill children: a systematic review. Clin. Nutr. 39(3), 685–693 (2020).
• Paper presents systematic review of feeding intolerance in critically ill children, and propose a standardized definition for use in clinical and research settings: “the inability to achieve enteral nutrition target intake in combination with the presence of gastrointestinal symptoms indicating gastrointestinal dysfunction”.
20.
Qiu CCC, Zhang W, Kou Q et al. Fat-modified enteral formula improves feeding tolerance in critically ill patients: a multicenter, single-blind, randomized controlled trial. JPEN J. Parenter. Enteral Nutr. 41(4), 785–795 (2017).
21.
Premier Applied Sciences, Premier, Inc. Premier Healthcare Database: data that informs and performs (White Paper). (2025). https://offers.premierinc.com/Premier-Healthcare-Database-Download.html
22.
Charlson ME, Pompei P, Ales KL et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40(5), 373–383 (1987).
23.
Quan H, Sundararajan V, Halfon P et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care 43(11), 1130–1139 (2005).
24.
Barrett JB, Trambley A, Blessinger EK et al. Reduced hospital readmissions through personalized care: implementation of a patient, risk-focused hospital-wide discharge care center. NEJM Catal Innov Care Deliv. 6(6), doi: (2025) (Epub ahead of print).
25.
Bechtold ML, Brown PM, Escuro A et al. When is enteral nutrition indicated? JPEN J. Parenter. Enteral Nutr. 46(7), 1470–1496 (2022).
• Paper presents the American Society of Parenteral and Enteral Nutrition (ASPEN) Enteral Nutrition Committee’s Consensus Statement including recommendations on when enteral nutrition is indicated for use.
26.
Schuetz P, Sulo S, Walzer S et al. Cost savings associated with nutritional support in medical inpatients: an economic model based on data from a systematic review of randomised trials. BMJ Open 11(7), e046402 (2021).
27.
Arensberg MB, Gahche JJ, Dwyer JT et al. Malnutrition-related conditions and interventions in US state/territorial older Americans act aging plans. BMC Geriatrics 22(1), 664 (2022).
28.
Meehan A, Partridge J, Jonnalagadda SS. Clinical and economic value of nutrition in healthcare: a nurse's perspective. Nutr. Clin. Pract. 34(6), 832–838 (2019).
29.
Liu H, Bao-yun S, Jin J et al. Length of stay, hospital costs and mortality associated with comorbidity according to the Charlson Comorbidity Index in immobile patients after ischemic stroke in China: a national study. Int. J. Health Policy Manag. 11(9), 1780–1787 (2021).
30.
Zhong Y, Cohen JT, Goates S et al. The cost-effectiveness of oral nutrition supplementation for malnourished older hospital patients. Appl. Health Econ. Health Policy 15(1), 75–83 (2017).
• Paper presents study of the cost–effectiveness of a randomized controlled trial that tested the use of a specialized oral nutrition supplement (ONS) in hospitalized malnourished older adults. Findings suggest ONS study cost effectively improved health in participants.
31.
Lakdawalla D, Snider JT, Perlroth D et al. Can oral nutritional supplements improve medicare patient outcomes in the hospital? Forum Health Econ. Policy 17(2), A503 (2014).
Information & Authors
Information
Published In
Copyright
© 2026 Kate Farms Inc. This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License
History
Received: 7 February 2026
Accepted: 14 May 2026
Published online: 26 May 2026
Keywords:
Topics
Authors
Metrics & Citations
Metrics
Article Usage
Article usage data only available from February 2023. Historical article usage data, showing the number of article downloads, is available upon request.
Citations
How to Cite
Comparing characteristics and outcomes between hospitalized adults on a pea protein or dairy/soy protein formulas: initial findings. (2026) Journal of Comparative Effectiveness Research. DOI: 10.57264/cer-2026-0035
Export citation
Select the citation format you wish to export for this article or chapter.
