The role of systemic inflammatory indices in predicting nausea and vomiting in pregnancy and the need for hospitalization

The role of systemic inflammatory indices in predicting nausea and vomiting in pregnancy and the need for hospitalization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The role of systemic inflammatory indices in predicting nausea and vomiting in pregnancy and the need for hospitalization Murat Levent Dereli, Sadullah Özkan, Belgin Savran Üçok, Serap Topkara, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013479/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the role of the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI) and pan-immune inflammation value (PIV) in predicting nausea and vomiting in pregnancy (NVP) Methods Women diagnosed and managed for NVP at a large tertiary hospital between 2016 and 2021 were retrospectively analyzed. After applying the inclusion criteria, a total of 278 eligible patients with NVP and 278 gestational age-matched healthy pregnant women were included. Patients with NVP were divided into mild (n = 58), moderate (n = 140) and severe NVP (n = 80). Patients with moderate and/or severe NVP who were at high risk for hospitalization were pooled and assigned to an inpatient treatment group. The groups were then compared. Results SII and PIV were significantly higher in the NVP group than in the control group, while SII, SIRI and PIV were significantly higher in the inpatient treatment group than in the mild NVP group. The comparison of overall performance in predicting NVP development showed that SII was better than PIV (p 1207x10 3 /µL (47.48% sensitivity, 82.01% specificity) had the highest discriminatory power for predicting a pregnancy in which NVP will develop. Conclusions Our results suggest an association between high SII and PIV and an increased risk of future NVP. These markers can be used as a first-trimester screening test to improve treatment planning of pregnancies at high risk of HG. Complete blood count hemogram hyperemesis gravidarum index pan-immune value severity Figures Figure 1 Figure 2 Take-home message Currently, there is no method or biomarker that can accurately predict hyperemesis gravidarum, and most of the biomarkers studied are not yet suitable for widespread clinical use due to their high cost and difficulty of application. The systemic immune-inflammation index, systemic inflammation response index and pan-immune inflammation value calculated from the first trimester hemogram may be helpful in predicting the increased risk of nausea and vomiting in pregnancy and the severity. INTRODUCTION Morning sickness, also known as mild nausea and vomiting in pregnancy (NVP), is a common symptom of pregnancy, especially in the first trimester. It is usually at its worst in the morning, but can occur at any other time of day [ 1 ]. Hyperemesis gravidarum (HG), on the other hand, is a condition associated with severe NVP and occurs in around 0.3-3% of all pregnancies [ 1 , 2 ]. HG can lead to serious problems such as dehydration, electrolyte imbalance, malnutrition, weight loss and psychological disturbances in the mother, as well as neurological and unknown consequences for the exposed fetus [ 3 – 5 ]. Moderate and severe NVP (also known as HG) is a common reason for hospitalization in early pregnancy, leading to loss of employment, absence from social life and impaired quality of life. Although there are various theories suggesting endocrinological factors (beta-human chorionic gonadotropin, estrogen and other placental hormones, thyroid hormones), genetic and familial predisposition and psychological susceptibility, the exact etiology for HG is not yet clear [ 6 ]. Pregnancies with increased placental tissue volume, such as molar and multiple pregnancies, and women with a history of motion sickness, migraine, a family or personal history of HG in previous pregnancies are at high risk for HG. As HG can be associated with severe maternal and fetal morbidity, prediction, early detection and appropriate treatment of pregnant women at high risk of HG is crucial to reduce maternal and fetal complications. During embryo implantation and trophoblastic invasion, a local proinflammatory response is triggered at the implantation site, which plays a role in maintaining further embryo invasion, differentiation, and placental development [ 7 , 8 ]. Any abnormality in this process can manifest itself as altered inflammation. Several inflammation biomarkers such as C-reactive protein (CRP), tumor necrosis factor alfa (TNF-α), interleucin-6 (IL -6), neopterin and vaspin have been studied and found to be partially involved in the etiology of HG [ 9 , 10 ]. Since these biomarkers cannot be used universally due to the high cost and difficulty of integrating them into clinical practice, inflammatory markers derived from a simple complete blood count (CBC) appear to be more attractive for predicting diseases and/or their prognosis where altered inflammation plays an important role in pathogenesis. In addition, the relationship between CBC-derived inflammatory parameters, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR), and ketonuria and the severity of HG has already been investigated [ 11 , 12 ]. Research in this area continues to search for more relevant, useful, and easily applicable predictive tools at a reasonable cost for predicting NVP and its severity. In this context, we aimed to investigate the prediction of women at high risk of developing moderate NVP and HG, which almost always lead to hospitalization in early pregnancy, by calculating inflammatory markers such as systemic immune inflammation index (SII), systemic inflammatory response index (SIRI) and pan-immune inflammation value (PIV) from first trimester CBC. METHODS Study Design We conducted a retrospective cohort study of 278 women with singleton pregnancies in the first trimester who were diagnosed with NVP between January 1, 2016, and December 31, 2021, at a large tertiary research and teaching hospital. We included 278 gestational age-matched healthy women with singleton pregnancies as controls. This study was conducted in accordance with the principles of the Declaration of Helsinki on human experimentation. After ethical approval of the study by the ethics committee for medical research of the local hospital (22.02.2022, 03/26), medical records were retrospectively reviewed. Definitions, Characteristics of Study Population, Patient Selection Pregnancy was divided into three periods called trimesters. Pregnancy up to the 14th week of gestation was referred to as the first trimester. The gestational age was calculated from the first day of the last menstrual period and confirmed by a sonographic measurement of the crown-rump length (CRL). If the calculated gestational age contained fractions of days, the gestational age was rounded up or down to the nearest whole week. Body mass index (BMI) was calculated as body weight in kilograms divided by the square of height in meters. Criteria for the diagnosis of HG were persistent vomiting not due to causes other than pregnancy, ketonuria in the urine test indicating acute starvation, and weight loss of at least 5% of pre-pregnancy weight [ 6 ]. The modified pregnancy-unique quantification of emesis and nausea (PUQE) index, which consists of the assessment of three components, including the duration of nausea in hours, the number of vomiting episodes, and retching within a day, was used to assess the severity of NVP [ 13 ]. The severity of nausea and/or vomiting in pregnancy is categorized using the PUQE score: mild (4–6), moderate (7–12) and severe, also known as HG (≥ 13). The patients with moderate NVP and HG, who were almost always treated as inpatients, were defined as patients requiring 'inpatient treatment'. The exclusion criteria were divided into two main categories, including situations associated with nausea and vomiting (gastroenteritis, cholecystitis, hepatitis and other diseases of the gastrointestinal tract, diabetic ketoacidosis, thyroid diseases, and neurological conditions that can cause increased intracranial pressure and induce vomiting) and inflammatory diseases (pelvic inflammatory disease, coronavirus infections and other acute infections, autoimmune diseases, liver and/or kidney failure, diabetes mellitus, cardiovascular diseases). Women with altered inflammatory status or altered platelet and leukocyte counts due to medical problems such as anti-inflammatory medications and/or corticosteroids, multiple pregnancies, molar pregnancies, CBC data from beyond the first trimester (beyond 13 weeks and six days), and women with missing data were also not included in the study. After applying the exclusion criteria, randomization for the control group was performed by enrolling gestational age-matched healthy pregnant women admitted to the outpatient clinic, in chronological order immediately following the admission of each woman enrolled in the NVP group. Imaging Methods, Laboratory Measurements and Study Variables Detection of intrauterine pregnancy, embryonic/fetal cardiac activity, and measurement of CRL were performed with the same sonography system [GE Voluson 730 Expert System (General Electric Medical Systems, Milwaukee, WI, USA) with a 4–8 MHz transabdominal probe or a 5–9 MHz transvaginal transducer]. On admission to the clinic, blood samples for CBC were collected in BD Vacutainer K2E tubes containing anticoagulant and analyzed within 30 minutes of receipt using a Mindray BC-6800 hematology analyzer (Mindray Medical International Limited, Shenzhen, Guangdong, China). Blood samples for analysis of biochemical parameters were placed in serum separator tubes containing gel and analyzed within 30 minutes of receipt using a Roche Cobas e801 chemiluminescence immunoassay analyzer (Roche Diagnostics International Limited, Rotkreuz, Switzerland). Blood indices and ratios of systemic inflammation, including SII, SIRI and PIV of each study participant were calculated based on CBC parameters using the following formulas: "SII = neutrophil count (µL) x platelet count (µL)/lymphocyte count (µL) [ 14 ]; SIRI = neutrophil count (µL) x monocyte count (µL)/lymphocyte count (µL) [ 15 ]; PIV = neutrophil count (µL) x platelet count (µL) x monocyte count (µL)/lymphocyte count (µL) [ 16 ]". Data Collection Clinical characteristics and medical history of participants, including age, BMI, comorbidities, gravidity, parity, gestational age; laboratory findings, including aspartate aminotransferase (AST), alanine transaminase (ALT), blood urea nitrogen (BUN), urine specific gravity, CBC parameters, CBC-derived inflammatory ratios; and sonographic findings such as the presence of embryonic/fetal cardiac activity, CRL measurements were obtained from medical records in the hospital database. Statistical analysis All statistical analyzes were performed using the R Statistical Software (version 2021.09.4 + 403.pro3; R Foundation for Statistical Computing, Vienna, Austria). Shapiro-Wilk tests were used to determine normality. Descriptive analyzes for the non-normally distributed numerical data were performed using medians and quartiles (Q1-Q3). Kruskal-Wallis and Mann-Whitney U tests were performed to compare these parameters between groups. Bonferroni correction was used to adjust for multiple comparisons. Descriptive analyzes for the categorical variables were performed using frequency and percentage. The relationships between categorical variables were analyzed with the chi-square test or Fisher’s exact test. The predictive power of various parameters that can be used to discriminate patients requiring inpatient treatment (moderate NVP and HG) was analyzed using receiver operating characteristics (ROC) curve analysis. When a significant cut-off value was determined, sensitivity, specificity, area under the curve (AUC), positive likelihood ratio and negative likelihood ratio were reported. The ROC curves and AUC values of these parameters were then compared with each other. A p-value of less than 0.05 was considered a statistically significant result. RESULTS A total of 556 eligible participants who met the inclusion criteria were included in the study, of whom 278 (100%) were diagnosed with NVP (NVP group) and 278 were gestational age-matched healthy pregnant women (control group). Patients with NVP were divided into three subgroups according to severity: 58 (20.9%), 140 (50.3%), and 80 (28.8%) patients with mild NVP, moderate NVP, and HG, respectively (Fig. 1 ). Baseline characteristics and clinical variables of participants in NVP and control groups were compared in Table 1 . Maternal age and gestational age were indifferent, while BMI, gravidity, and parity were significantly higher in control group (p = 0.001 for all). A further comparison of baseline characteristics and clinical variables between the three subgroups of NVP classified by severity revealed no significant differences (Table 2 ). Table 1 Comparison of clinical characteristics and laboratory findings of patients on hospital admission between nausea and vomiting in pregnancy and control groups Variable NVP Group (n = 278) Control Group (n = 278) p Maternal age (years) 26 (22-29.2) 27 (23–31) 0.059 BMI (kg/m 2 ) 25 (21–31) 28.5 (26–31) < 0.001 Gravida (number) 1 (1–2) 2 (1–3) < 0.001 Parity (number) 0 (0–1) 1 (0–2) < 0.001 Gestational age (weeks) 10 (8–12) 10 (8–11) 0.482 SII (10 3 /µL) 1173 (771–1702) 825 (662–1125) < 0.001 SIRI (10 3 /µL) 1.9 (1.3–2.7) 1.9 (1.4–2.5) 0.743 PIV (10 6 /µL 2 ) 487.1 (321.7-816.8) 424.2 (295.2-665.5) 0.009 ALT (IU/L) 14 (10–23) 10 (8–13) < 0.001 AST (IU/L) 18 (15–22) 17 (15–21) 0.283 Creatinine (mg/dL) 0.5 (0.4-05) 0.5 (0.4-05) 0.109 BUN (mg/dL) 8 (7-10.6) 7 (6–9) < 0.001 Urine specific gravity 1025 (1020–1030) 1020 (1016–1024) < 0.001 ALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body-mass index; BUN, blood urea nitrogen; g/dL, grams per deciliter; HG, hyperemesis gravidarum; IU/L, international units per liter; kg/m 2 , kilograms per square meter; mg/dL, milligrams per deciliter; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; µL, microliter. Data are expressed as median (quartile 1-quartile 3). A p value of < 0.05 indicates a significant difference. Statistically significant p-values are in bold. Table 2 Demographic and clinical characteristics of patients with different subgroups of NVP Variable Mild NVP (n = 58) Moderate NVP (n = 140) Severe NVP (HG) (n = 80) p Age (years) 27 (22–30) 26 (22–29) 26 (23–30) 0.677 BMI (kg/m 2 ) 27 (22–31) 24 (21–31) 26 (23–31) 0.212 Gravida (number) 1 (1–2) 1 (1–2) 1 (1–2) 0.420 Parity (number) 0 (0–1) 0 (0–1) 0 (0–1) 0.405 Gestational age (weeks) 9 (7–12) 9 (7–12) 10 (9–12) 0.454 SII (10 3 /µL) 808 (677–1397) 1209 (764–1703) 1234 (910–1792) 0.003 * SIRI (10 3 /µL) 1.50 (1.00-2.21) 1.94 (1.29–2.76) 1.95 (1.48–3.38) 0.009 * PIV (10 6 /µL 2 ) 376 (264–582) 513 (347–819) 516 (368–950) 0.007 * ALT (IU/L) 17 (12–26) 13 (10–23) 13 (10–20) 0.053 AST (IU/L) 19 (15–27) 18 (15–21) 18 (15–21) 0.113 Creatinine (mg/dL) 0.5 (0.4–0.5) 0.5 (0.4–0.5) 0.5 (0.4–0.5) 0.848 BUN (mg/dL) 8.3 (6.1–10) 8 (7–11) 8.7 (7-10.7) 0.844 Urine specific gravity 1028 (1020–1030) 1025 (1020–1030) 1025 (1020–1030) 0.206 ALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body-mass index; BUN, blood urea nitrogen; g/dL, grams per deciliter; HG, hyperemesis gravidarum; IU/L, international units per liter; kg/m 2 , kilograms per square meter; mg/dL, milligrams per deciliter; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; µL, microliter. Data are expressed as median (quartile 1-quartile 3). A p value of < 0.05 indicates a significant difference. Statistically significant p-values are in bold. * : significant differences between Mild NVP vs. Moderate NVP and Mild NVP vs. Severe NVP (HG) groups The median values for ALT, BUN and urine specific gravity were significantly higher in NVP group [14 (10–23) vs. 10 (8–13), p < 0.001; 8 (7-10.6) vs. 7 (6–9), p < 0.001; and 1025 (1020–1030) vs. 1020 (1016–1024), p < 0.001, respectively], while the median values for AST and creatinine were indifferent. Of the blood count-derived inflammatory parameters, median SII and PIV were significantly higher in NVP group than in control group [1173 (771–1702) vs. 825 (662–1125), p < 0.001; and 487.1 (321.7-816.8) vs. 424.2 (295.2-665.5), p = 0.009], while median SIRI was indifferent between groups (Table 1 ). On the other hand, SII, SIRI and PIV were significantly higher in both the moderate NVP and HG groups than in the mild NVP group [(p = 0.017, 0.040 and 0.038, respectively) and (p = 0.003, 0.009 and 0.006, respectively)], while there were no significant differences between the moderate NVP and HG groups (Table 2 ). According to the ROC curve analysis performed for the discriminatory power of SII, and PIV to predict a pregnancy in which NVP will develop, the AUC values were 0.685 and 0.564, respectively. The cut-off values for SII, and PIV were > 1207x10 3 /µL (47.48% sensitivity, 82.01% specificity) and > 783x10 6 /µL 2 (27.34% sensitivity, 86.33% specificity), respectively. In addition, ROC curve analysis for discriminatory power of SII, SIRI and PIV to predict pregnancy with NVP requiring hospitalization yielded AUC values of 0.639, 0.625 and 0.627, respectively. The cut-off values for SII, SIRI and PIV were > 1056x10 3 /µL (64.55% sensitivity, 68.97% specificity), 32.12x10 3 /µL 2 (77.27% sensitivity, 43.10% specificity), and > 350x10 6 /µL (77.27% sensitivity, 46.55% specificity), respectively (Table 3 ). Comparisons of the ROC curves of these indices for predicting NVP and inpatient treatment needs are shown in Fig. 2 . Table 3 ROC curve analysis for various parameters derived from the CBC that can be used to predict NVP and the need for inpatient treatment Variable AUC CI 95% p Cut-off value Sensitivity (%) Specificity (%) + LHR - LHR NVP prediction SII (10 3 /µL) 0.685 0.644 − 0.623 1207 47.48 82.01 2.64 0.64 PIV (10 6 /µL 2 ) 0.564 0.522–0.606 0.008 > 783 27.34 86.33 2.00 0.84 Inpatient treatment Prediction SII (10 3 /µL) 0.639 0.580–0.696 0.002 > 1056 64.55 68.97 2.08 0.51 SIRI (10 3 /µL) 0.625 0.565–0.682 0.003 32.12 77.27 43.10 1.36 0.53 PIV (10 6 /µL 2 ) 0.627 0.568–0.684 0.003 > 350.19 77.27 46.55 1.45 0.49 AUC, area under the curve; CBC, complete blood count; CI, confidence interval; LHR, likelihood ratio; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; ROC, receiver operating characteristic; SII, systemic immune-inflammatory index; SIRI, systemic inflammation response index; µL, microliter. A p value of < 0.05 indicates a significant difference. Statistically significant p-values are in bold. The comparison of overall performance in predicting NVP development showed that SII was better than PIV (p < 0.001), while there was no significant superiority between SII, SIRI and PIV in predicting inpatient treatment needs (Table 4 ). Table 4 Comparison of CBC-derived inflammatory indices that can be used to predict NVP and the need for inpatient treatment Variable NVP prediction Inpatient treatment prediction SII PIV SII SIRI PIV SII < 0.001 0.661 0.698 SIRI 0.661 0.872 PIV < 0.001 0.698 0.872 AUC, area under the curve; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammation response index. A p value of < 0.05 indicates a significant difference. Statistically significant p-values are in bold. For comparisons where the p-value is significant, the parameter with the higher AUC value in Table 3 is superior in prediction. DISCUSSION While mild NVP is a common pregnancy condition, especially in the first trimester, the etiopathologic mechanism leading to severe NVP, also known as HG, is not fully understood and several factors are thought to play a role in the etiology [ 17 ]. The risk of HG is increased by known risk factors such as multiple pregnancies, molar pregnancies and pre-pregnancy risk factors such as underweight, primiparity, hyperthyroidism, asthma, motion sickness and gastrointestinal disorders [ 18 – 21 ]. However, current knowledge of the other risk factors is limited as the studies investigating HG generally have a small sample size, leading to inconclusive results. In this respect, HG is very well defined by a recently published study with a large sample size over a long period of time. It concludes that pregnancies diagnosed with HG differ not only from pregnancies of women who never diagnosed with HG, but also from HG patients’ other, non-hyperemesis gravidarum pregnancies [ 22 ]. Prediction and early diagnosis of HG is crucial to avoid serious maternal, fetal and neonatal consequences such as vitamin B1 and K deficiency, Wernicke's encephalopathy, dehydration, electrolyte imbalance, malnutrition, preterm birth, fetal growth restriction and yet unknown consequences for the offspring [ 3 , 23 – 28 ]. This is all the more true for pregnant women in rural areas who live far away from health centers and do not have the means to visit them frequently enough, as well as for women who do not seek medical care due to cultural and financial constraints [ 29 ]. Although several studies have been conducted with biomarkers such as leptin, nesfatine-1, ghrelin, obestatin and prealbumin, their role in predicting HG is unclear due to the inconsistent results and small sample size of the studies [ 17 , 30 – 33 ]. Therefore, there is currently no generally accepted tool in the clinical setting that can accurately predict the development of HG. Leptin levels in women with HG compared to women in the control group were found to be higher, similar or lower in various studies [ 30 , 31 , 33 ]. Similarly, nisfatin-1 levels were higher in women with HG compared to women in the control group in the Gungor et al. study, while Ozturk et al. found no difference [ 32 , 33 ]. On the other hand, some studies found that beta-human chorionic gonadotropin (beta-hCG), thyroxine and its free form were elevated in pregnancies with HG, indicating a strong association between these variables and HG [ 34 , 35 ]. The sample sizes in all these studies were not large enough and BMI-adjusted values could not be compared because no data on the patients' BMI measurements were available. In addition to the biomarkers, ultrasound markers were also investigated for HG prediction. A study by Yıldız et al. found that placental thickness and free beta-hCG levels were higher in patients with HG [ 36 ]. It is already known that a high beta-hCG level is associated with HG. Since placental mass correlates with beta-hCG levels, it is obvious that placental mass is indirectly related to HG. However, accurate measurement of placental thickness may not be possible depending on many factors, including subcutaneous adipose tissue thickness, placental localization, intra- and interobserver variability, and the difficulty of standardizing the part to be measured. As inflammatory markers derived from a single CBC have gained attention in recent years and the search for inexpensive, widely available, easily applicable and interpretable biomarkers that can more accurately predict the development, prognosis and/or severity of diseases with altered inflammation such as HG continues, we conducted this article. Furthermore, to our knowledge, this article is the first study to investigate SIRI and PIV as predictive markers for predicting women who develop NVP and its severity. As mentioned in the introduction, the hypothesis of the current study was based on altered inflammation, which seems to play an important role in the development of HG. Inflammatory indices and cell lines derived from the CBC have been the subject of research for NVP. In the study by Tayfur et al., the neutrophil, lymphocyte and platelet counts, NLR, PLR and plateletcrit differed significantly between the HG and control groups [ 37 ]. However, as altered inflammation is a complex process, it would not be realistic, repeatable or meaningful to make decisions based on a single cell line such as neutrophils, lymphocytes or platelets alone. In this context, we investigated SII, SIRI and PIV, which collectively assess different individual inflammatory cell lines and have previously been studied for the prediction and prognosis of many diseases in which inflammation plays a role in pathogenesis [ 15 , 16 , 38 ]. Furthermore, in the above-mentioned study, all women with NVP of varying severity were grouped under the term 'HG', which should only refer to patients with severe NVP as defined in the same study [ 37 ]. Moreover, the range of gestational age of the participants when the CBC data were collected in the study was quite wide (before 22 weeks gestation) [ 37 ]. Since the composition of the CBC parameters varies with increasing gestational age, the inclusion of pregnant women with a wide range of gestational ages leads to less consistency in the results. In line with the literature, the results of our study showed a higher BMI, higher gravidity and higher parity in patients who did not have NVP. Higher BMI is associated with lower NVP rates, and since previous NVP is a significant risk factor for recurrence of NVP, it is not surprising that women who have experienced NVP and HG are less likely to want to become pregnant again [ 39 , 40 ]. Main findings of the present study were: (1) SII and PIV were significantly higher in the NVP group than in the control group. An SII with a cut-off value of > 1207x10 3 /µL had a higher discriminatory power in predicting NVP than a PIV with a cut-off value of > 783x10 6 /µL 2 . (2) SII, SIRI and PIV were significantly higher in both the moderate and severe NVP groups than in the mild NVP group, while there were no significant differences between the moderate NVP and HG groups. However, distinguishing who will develop moderate or severe NVP (HG) in patients predicted to have NVP does not contribute to clinical practice. For this reason, although their predictive power is the same, we can use SII, SIRI and PIV to differentiate between patients with mild NVP and patients with moderate and severe NVP, who almost always require hospitalization. Although these calculations seem complicated, the results can be integrated into the CBC reports using software programming. Our study has some limitations, mainly due to its retrospective nature. Major strength of this study is that it was conducted in a large tertiary referral hospital using the same algorithms for diagnosis, treatment and follow-up. Furthermore, since all participants were randomly assigned to the control group according to the respective time of hospital admission after each patient with NVP, the impact of bias on the results was very small despite the retrospective nature of the study. In conclusion, our results showed an association between high SII and PIV and an increased risk of NVP. In addition, distinguishing between patients with mild and moderate and/or severe NVP with SII, SIRI and PIV can facilitate decision making in treatment planning, as patients in the latter group are at high risk of morbidity and almost always require inpatient treatment. Further prospective, randomized, controlled studies are needed to better define the efficacy and limitations of these markers. Declarations Author contribution ML Dereli: Study design, project development, analysis strategy, manuscript writing S Özkan: Project development, data analysis B Savran Üçok: Data collection S Topkara: Statistical analysis, manuscript writing S Sucu: Statistical analysis, manuscript writing FB Fıratlıgil: Statistical analysis, literature search D Kurt: Data collection A Kurt: Data collection Ş Çelen: Proofreading, language support Y Engin Üstün: Proofreading, language support STATEMENTS AND DECLARATIONS Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was conducted in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Health Sciences, Ankara Etlik Lady Zübeyde Training and Research Hospital, Ankara, Turkey (Date:22.02.2022, No:03/26). The study was conducted at the Department of Obstetrics and Gynecology, Ankara Etlik Lady Zübeyde Training and Research Hospital, Ankara, Turkey Consent to participate and publish The Ethics Committee of University of Health Sciences, Ankara Etlik Lady Zübeyde Training and Research Hospital, Ankara, Turkey waived the requirement to obtain informed consent because of the retrospective nature of the study. Availability of Data The data that support the findings of this study are openly available in “figshare” at http://doi.org/10.6084/m9.figshare.24996407 References Boelig RC, Barton SJ, Saccone G, Kelly AJ, Edwards SJ, Berghella V (2018) Interventions for treating hyperemesis gravidarum: a Cochrane systematic review and meta-analysis. 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Br J Cancer 123(3):403–409. https://doi.org/10.1038/s41416-020-0894-7 Ioannidou P, Papanikolaou D, Mikos T, Mastorakos G, Goulis DG (2019) Predictive factors of Hyperemesis Gravidarum: A systematic review. Eur J Obstet Gynecol Reprod Biol 238:178–187. https://doi.org/10.1016/j.ejogrb.2019.04.043 Fell DB, Dodds L, Joseph KS, Allen VM, Butler B (2006) Risk factors for hyperemesis gravidarum requiring hospital admission during pregnancy. Obstet Gynecol 107(2 Pt 1):277–284. https://doi.org/10.1097/01.AOG.0000195059.82029.74 Kim HY, Cho GJ, Kim SY, Lee KM, Ahn KH, Han SW, Hong SC, Ryu HM, Oh MJ, Kim HJ, Kim SC (2020) )Pre-Pregnancy Risk Factors for Severe Hyperemesis Gravidarum: Korean Population Based Cohort Study. Life (Basel) 11(1):12. https://doi.org/10.3390/life11010012 Gill SK, Maltepe C, Koren G (2009) The effect of heartburn and acid reflux on the severity of nausea and vomiting of pregnancy. Can J Gastroenterol 23(4):270–272. https://doi.org/10.1155/2009/678514 Basso O, Olsen J (2001) Sex ratio and twinning in women with hyperemesis or pre-eclampsia. Epidemiology 12(6):747–749. https://doi.org/10.1097/00001648-200111000-00026 Nurmi M, Rautava P, Gissler M, Vahlberg T, Polo-Kantola P (2020) Incidence and risk factors of hyperemesis gravidarum: A national register-based study in Finland, 2005–2017. Acta Obstet Gynecol Scand 99(8):1003–1013. https://doi.org/10.1111/aogs.13820 Selitsky T, Chandra P, Schiavello HJ (2006) Wernicke's encephalopathy with hyperemesis and ketoacidosis. Obstet Gynecol 107:486–490. https://doi.org/10.1097/01.AOG.0000172373.41828.8a Kohnke S, Meek CL (2021) Don't seek, don't find: The diagnostic challenge of Wernicke's encephalopathy. Ann Clin Biochem 58(1):38–46. https://doi.org/10.1177/0004563220939604 Nijsten K, van der Minnen L, Wiegers HMG, Koot MH, Middeldorp S, Roseboom TJ, Grooten IJ, Painter RC (2022) Hyperemesis gravidarum and vitamin K deficiency: a systematic review. Br J Nutr 128(1):30–42. https://doi.org/10.1017/S0007114521002865 Agmon N, Sade S, Pariente G, Rotem R, Weintraub AY (2019) Hyperemesis gravidarum and adverse pregnancy outcomes. Arch Gynecol Obstet 300(2):347–353. https://doi.org/10.1007/s00404-019-05192-y Koudijs HM, Savitri AI, Browne JL, Amelia D, Baharuddin M, Grobbee DE, Uiterwaal CS (2016) Hyperemesis gravidarum and placental dysfunction disorders. BMC Pregnancy Childbirth 16(1):374. https://doi.org/10.1186/s12884-016-1174-7 Koren G, Ornoy A, Berkovitch M (2018) Hyperemesis gravidarum-Is it a cause of abnormal fetal brain development? Reprod Toxicol 79:84–88. https://doi.org/10.1016/j.reprotox.2018.06.008 Douthit N, Kiv S, Dwolatzky T, Biswas S (2015) Exposing some important barriers to health care access in the rural USA. Public Health 129(6):611–620. https://doi.org/10.1016/j.puhe.2015.04.001 Demir B, Erel CT, Haberal A, Oztürk N, Güler D, Koçak M (2006) Adjusted leptin level (ALL) is a predictor for hyperemesis gravidarum. Eur J Obstet Gynecol Reprod Biol 124(2):193–196. https://doi.org/10.1016/j.ejogrb.2004.11.012 Aka N, Atalay S, Sayharman S, Kiliç D, Köse G, Küçüközkan T (2006) Leptin and leptin receptor levels in pregnant women with hyperemesis gravidarum. Aust N Z J Obstet Gynaecol 46(4):274–277. https://doi.org/10.1111/j.1479-828X.2006.00590.x Ozturk G, Ozgu-Erdinc AS, Ucar F, Ginis Z, Erden G, Danisman N (2017) Concentrations of prealbumin and some appetite-controlling hormones in pregnancies associated with hyperemesis gravidarium. Ann Clin Biochem 54(2):258–263. https://doi.org/10.1177/0004563216654724 Gungor S, Gurates B, Aydin S, Sahin I, Kavak SB, Kumru S, Celik H, Aksoy A, Yilmaz M, Catak Z, Citil C, Baykus Y, Deniz R, Karakaya F, Özdemir N (2013) Ghrelins, obestatin, nesfatin-1 and leptin levels in pregnant women with and without hyperemesis gravidarum. Clin Biochem 46(9):828–830. https://doi.org/10.1016/j.clinbiochem.2013.01.015 Panesar NS, Li CY, Rogers MS (2001) Are thyroid hormones or hCG responsible for hyperemesis gravidarum? A matched paired study in pregnant Chinese women. Acta Obstet Gynecol Scand 80(6):519–524 Al-Yatama M, Diejomaoh M, Nandakumaran M, Monem RA, Omu AE, Al Kandari F (2002) Hormone profile of Kuwaiti women with hyperemesis gravidarum. Arch Gynecol Obstet 266(4):218–222. https://doi.org/10.1007/s004040100210 Yıldız G, Mat E, Kurt D, Yıldız P, Başol G, Gündoğdu EC, Kuru B, Turan K, Kale A (2022) Hyperemesis gravidarum and its relationships with placental thickness, PAPP-A and free beta-HCG: a case control study. South Clin Ist Euras 33(4):406–412. https://doi.org/10.14744/scie.2021.93546 Tayfur C, Burcu DC, Gulten O, Betul D, Tugberk G, Onur O, Engin K, Orcun O (2017) Association between platelet to lymphocyte ratio, plateletcrit and the presence and severity of hyperemesis gravidarum. J Obstet Gynaecol Res 43(3):498–504. https://doi.org/10.1111/jog.13228 Chen JH, Zhai ET, Yuan YJ, Wu KM, Xu JB, Peng JJ, Chen CQ, He YL, Cai SR (2017) Systemic immune-inflammation index for predicting prognosis of colorectal cancer. World J Gastroenterol 23(34):6261–6272. https://doi.org/10.3748/wjg.v23.i34.6261 Ben-Aroya Z, Lurie S, Segal D, Hallak M, Glezerman M (2005) Association of nausea and vomiting in pregnancy with lower body mass index. Eur J Obstet Gynecol Reprod Biol 118(2):196–198. https://doi.org/10.1016/j.ejogrb.2004.04.026 Lindström VS, Laitinen LM, Nurmi JMA, Koivisto MA, Polo-Kantola P (2023) Hyperemesis gravidarum: Associations with personal and family history of nausea. Acta Obstet Gynecol Scand 102(9):1176–1182. https://doi.org/10.1111/aogs.14629 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4013479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278324704,"identity":"09a88a98-931f-430e-845a-44b2050d07c7","order_by":0,"name":"Murat Levent 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17:32:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-4013479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52624902,"identity":"1d0f1bba-febd-46f9-89e4-30c416a38968","added_by":"auto","created_at":"2024-03-13 17:32:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384163,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study groups\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4013479/v1/f95322a39662a7c38db3379a.jpg"},{"id":52624903,"identity":"a2f01e32-3fd1-4ec4-9a08-c0bfa06708f4","added_by":"auto","created_at":"2024-03-13 17:32:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68034,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the receiver operating characteristic (ROC) curves of SII, SIRI and PIV for the prediction of NVP (a) and inpatient treatment need (b)\u003c/p\u003e","description":"","filename":"Figure02.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4013479/v1/bc166a408851f936b027d94a.jpg"},{"id":57159981,"identity":"f398d41d-239d-4f61-b47c-00205f158346","added_by":"auto","created_at":"2024-05-26 12:14:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013479/v1/591c00e6-fc77-4b5d-9eb8-de377394ffc6.pdf"}],"financialInterests":"","formattedTitle":"The role of systemic inflammatory indices in predicting nausea and vomiting in pregnancy and the need for hospitalization","fulltext":[{"header":"Take-home message","content":"\u003cp\u003eCurrently, there is no method or biomarker that can accurately predict hyperemesis gravidarum, and most of the biomarkers studied are not yet suitable for widespread clinical use due to their high cost and difficulty of application. The systemic immune-inflammation index, systemic inflammation response index and pan-immune inflammation value calculated from the first trimester hemogram may be helpful in predicting the increased risk of nausea and vomiting in pregnancy and the severity.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eMorning sickness, also known as mild nausea and vomiting in pregnancy (NVP), is a common symptom of pregnancy, especially in the first trimester. It is usually at its worst in the morning, but can occur at any other time of day [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hyperemesis gravidarum (HG), on the other hand, is a condition associated with severe NVP and occurs in around 0.3-3% of all pregnancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. HG can lead to serious problems such as dehydration, electrolyte imbalance, malnutrition, weight loss and psychological disturbances in the mother, as well as neurological and unknown consequences for the exposed fetus [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModerate and severe NVP (also known as HG) is a common reason for hospitalization in early pregnancy, leading to loss of employment, absence from social life and impaired quality of life. Although there are various theories suggesting endocrinological factors (beta-human chorionic gonadotropin, estrogen and other placental hormones, thyroid hormones), genetic and familial predisposition and psychological susceptibility, the exact etiology for HG is not yet clear [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Pregnancies with increased placental tissue volume, such as molar and multiple pregnancies, and women with a history of motion sickness, migraine, a family or personal history of HG in previous pregnancies are at high risk for HG. As HG can be associated with severe maternal and fetal morbidity, prediction, early detection and appropriate treatment of pregnant women at high risk of HG is crucial to reduce maternal and fetal complications.\u003c/p\u003e \u003cp\u003eDuring embryo implantation and trophoblastic invasion, a local proinflammatory response is triggered at the implantation site, which plays a role in maintaining further embryo invasion, differentiation, and placental development [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Any abnormality in this process can manifest itself as altered inflammation. Several inflammation biomarkers such as C-reactive protein (CRP), tumor necrosis factor alfa (TNF-α), interleucin-6 (IL -6), neopterin and vaspin have been studied and found to be partially involved in the etiology of HG [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Since these biomarkers cannot be used universally due to the high cost and difficulty of integrating them into clinical practice, inflammatory markers derived from a simple complete blood count (CBC) appear to be more attractive for predicting diseases and/or their prognosis where altered inflammation plays an important role in pathogenesis. In addition, the relationship between CBC-derived inflammatory parameters, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and monocyte-to-lymphocyte ratio (MLR), and ketonuria and the severity of HG has already been investigated [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Research in this area continues to search for more relevant, useful, and easily applicable predictive tools at a reasonable cost for predicting NVP and its severity. In this context, we aimed to investigate the prediction of women at high risk of developing moderate NVP and HG, which almost always lead to hospitalization in early pregnancy, by calculating inflammatory markers such as systemic immune inflammation index (SII), systemic inflammatory response index (SIRI) and pan-immune inflammation value (PIV) from first trimester CBC.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003eStudy Design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe conducted a retrospective cohort study of 278 women with singleton pregnancies in the first trimester who were diagnosed with NVP between January 1, 2016, and December 31, 2021, at a large tertiary research and teaching hospital. We included 278 gestational age-matched healthy women with singleton pregnancies as controls. This study was conducted in accordance with the principles of the Declaration of Helsinki on human experimentation. After ethical approval of the study by the ethics committee for medical research of the local hospital (22.02.2022, 03/26), medical records were retrospectively reviewed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinitions, Characteristics of Study Population, Patient Selection\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePregnancy was divided into three periods called trimesters. Pregnancy up to the 14th week of gestation was referred to as the first trimester. The gestational age was calculated from the first day of the last menstrual period and confirmed by a sonographic measurement of the crown-rump length (CRL). If the calculated gestational age contained fractions of days, the gestational age was rounded up or down to the nearest whole week. Body mass index (BMI) was calculated as body weight in kilograms divided by the square of height in meters. Criteria for the diagnosis of HG were persistent vomiting not due to causes other than pregnancy, ketonuria in the urine test indicating acute starvation, and weight loss of at least 5% of pre-pregnancy weight [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The modified pregnancy-unique quantification of emesis and nausea (PUQE) index, which consists of the assessment of three components, including the duration of nausea in hours, the number of vomiting episodes, and retching within a day, was used to assess the severity of NVP [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The severity of nausea and/or vomiting in pregnancy is categorized using the PUQE score: mild (4\u0026ndash;6), moderate (7\u0026ndash;12) and severe, also known as HG (\u0026ge;\u0026thinsp;13). The patients with moderate NVP and HG, who were almost always treated as inpatients, were defined as patients requiring 'inpatient treatment'.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were divided into two main categories, including situations associated with nausea and vomiting (gastroenteritis, cholecystitis, hepatitis and other diseases of the gastrointestinal tract, diabetic ketoacidosis, thyroid diseases, and neurological conditions that can cause increased intracranial pressure and induce vomiting) and inflammatory diseases (pelvic inflammatory disease, coronavirus infections and other acute infections, autoimmune diseases, liver and/or kidney failure, diabetes mellitus, cardiovascular diseases). Women with altered inflammatory status or altered platelet and leukocyte counts due to medical problems such as anti-inflammatory medications and/or corticosteroids, multiple pregnancies, molar pregnancies, CBC data from beyond the first trimester (beyond 13 weeks and six days), and women with missing data were also not included in the study.\u003c/p\u003e \u003cp\u003eAfter applying the exclusion criteria, randomization for the control group was performed by enrolling gestational age-matched healthy pregnant women admitted to the outpatient clinic, in chronological order immediately following the admission of each woman enrolled in the NVP group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImaging Methods, Laboratory Measurements and Study Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDetection of intrauterine pregnancy, embryonic/fetal cardiac activity, and measurement of CRL were performed with the same sonography system [GE Voluson 730 Expert System (General Electric Medical Systems, Milwaukee, WI, USA) with a 4\u0026ndash;8 MHz transabdominal probe or a 5\u0026ndash;9 MHz transvaginal transducer]. On admission to the clinic, blood samples for CBC were collected in BD Vacutainer K2E tubes containing anticoagulant and analyzed within 30 minutes of receipt using a Mindray BC-6800 hematology analyzer (Mindray Medical International Limited, Shenzhen, Guangdong, China). Blood samples for analysis of biochemical parameters were placed in serum separator tubes containing gel and analyzed within 30 minutes of receipt using a Roche Cobas e801 chemiluminescence immunoassay analyzer (Roche Diagnostics International Limited, Rotkreuz, Switzerland).\u003c/p\u003e \u003cp\u003eBlood indices and ratios of systemic inflammation, including SII, SIRI and PIV of each study participant were calculated based on CBC parameters using the following formulas: \"SII\u0026thinsp;=\u0026thinsp;neutrophil count (\u0026micro;L) x platelet count (\u0026micro;L)/lymphocyte count (\u0026micro;L) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; SIRI\u0026thinsp;=\u0026thinsp;neutrophil count (\u0026micro;L) x monocyte count (\u0026micro;L)/lymphocyte count (\u0026micro;L) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; PIV\u0026thinsp;=\u0026thinsp;neutrophil count (\u0026micro;L) x platelet count (\u0026micro;L) x monocyte count (\u0026micro;L)/lymphocyte count (\u0026micro;L) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\".\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClinical characteristics and medical history of participants, including age, BMI, comorbidities, gravidity, parity, gestational age; laboratory findings, including aspartate aminotransferase (AST), alanine transaminase (ALT), blood urea nitrogen (BUN), urine specific gravity, CBC parameters, CBC-derived inflammatory ratios; and sonographic findings such as the presence of embryonic/fetal cardiac activity, CRL measurements were obtained from medical records in the hospital database.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyzes were performed using the R Statistical Software (version 2021.09.4\u0026thinsp;+\u0026thinsp;403.pro3; R Foundation for Statistical Computing, Vienna, Austria). Shapiro-Wilk tests were used to determine normality. Descriptive analyzes for the non-normally distributed numerical data were performed using medians and quartiles (Q1-Q3). Kruskal-Wallis and Mann-Whitney U tests were performed to compare these parameters between groups. Bonferroni correction was used to adjust for multiple comparisons. Descriptive analyzes for the categorical variables were performed using frequency and percentage. The relationships between categorical variables were analyzed with the chi-square test or Fisher\u0026rsquo;s exact test. The predictive power of various parameters that can be used to discriminate patients requiring inpatient treatment (moderate NVP and HG) was analyzed using receiver operating characteristics (ROC) curve analysis. When a significant cut-off value was determined, sensitivity, specificity, area under the curve (AUC), positive likelihood ratio and negative likelihood ratio were reported. The ROC curves and AUC values of these parameters were then compared with each other. A p-value of less than 0.05 was considered a statistically significant result.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 556 eligible participants who met the inclusion criteria were included in the study, of whom 278 (100%) were diagnosed with NVP (NVP group) and 278 were gestational age-matched healthy pregnant women (control group). Patients with NVP were divided into three subgroups according to severity: 58 (20.9%), 140 (50.3%), and 80 (28.8%) patients with mild NVP, moderate NVP, and HG, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Baseline characteristics and clinical variables of participants in NVP and control groups were compared in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Maternal age and gestational age were indifferent, while BMI, gravidity, and parity were significantly higher in control group (p\u0026thinsp;=\u0026thinsp;0.001 for all). A further comparison of baseline characteristics and clinical variables between the three subgroups of NVP classified by severity revealed no significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics and laboratory findings of patients on hospital admission between nausea and vomiting in pregnancy and control groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNVP Group (n\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal age (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22-29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (23\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (21\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 (26\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravida (number)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity (number)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational age (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1173 (771\u0026ndash;1702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e825 (662\u0026ndash;1125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIRI (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (1.3\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.4\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIV (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e487.1 (321.7-816.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e424.2 (295.2-665.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT (IU/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (10\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST (IU/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (15\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (15\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.4-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.4-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7-10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrine specific gravity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1025 (1020\u0026ndash;1030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1020 (1016\u0026ndash;1024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body-mass index; BUN, blood urea nitrogen; g/dL, grams per deciliter; HG, hyperemesis gravidarum; IU/L, international units per liter; kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003ekilograms per square meter; mg/dL, milligrams per deciliter; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; \u0026micro;L, microliter. Data are expressed as median (quartile 1-quartile 3). A p value of \u0026lt;\u0026thinsp;0.05 indicates a significant difference. Statistically significant p-values are in bold.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients with different subgroups of NVP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild NVP\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate NVP\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere NVP (HG)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (22\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (22\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (23\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (22\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (21\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (23\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravida (number)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity (number)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGestational age (weeks)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (9\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e808 (677\u0026ndash;1397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1209 (764\u0026ndash;1703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1234 (910\u0026ndash;1792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIRI (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.50 (1.00-2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (1.29\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95 (1.48\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIV (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376 (264\u0026ndash;582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e513 (347\u0026ndash;819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e516 (368\u0026ndash;950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eALT (IU/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (12\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (10\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (10\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAST (IU/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (15\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (15\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (15\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBUN (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3 (6.1\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7 (7-10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrine specific gravity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1028 (1020\u0026ndash;1030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1025 (1020\u0026ndash;1030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1025 (1020\u0026ndash;1030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body-mass index; BUN, blood urea nitrogen; g/dL, grams per deciliter; HG, hyperemesis gravidarum; IU/L, international units per liter; kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003ekilograms per square meter; mg/dL, milligrams per deciliter; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammatory response index; \u0026micro;L, microliter. Data are expressed as median (quartile 1-quartile 3). A p value of \u0026lt;\u0026thinsp;0.05 indicates a significant difference. Statistically significant p-values are in bold.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e: \u003cem\u003esignificant differences between Mild NVP vs. Moderate NVP and Mild NVP vs. Severe NVP (HG) groups\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe median values for ALT, BUN and urine specific gravity were significantly higher in NVP group [14 (10\u0026ndash;23) vs. 10 (8\u0026ndash;13), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 8 (7-10.6) vs. 7 (6\u0026ndash;9), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and 1025 (1020\u0026ndash;1030) vs. 1020 (1016\u0026ndash;1024), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively], while the median values for AST and creatinine were indifferent. Of the blood count-derived inflammatory parameters, median SII and PIV were significantly higher in NVP group than in control group [1173 (771\u0026ndash;1702) vs. 825 (662\u0026ndash;1125), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and 487.1 (321.7-816.8) vs. 424.2 (295.2-665.5), p\u0026thinsp;=\u0026thinsp;0.009], while median SIRI was indifferent between groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the other hand, SII, SIRI and PIV were significantly higher in both the moderate NVP and HG groups than in the mild NVP group [(p\u0026thinsp;=\u0026thinsp;0.017, 0.040 and 0.038, respectively) and (p\u0026thinsp;=\u0026thinsp;0.003, 0.009 and 0.006, respectively)], while there were no significant differences between the moderate NVP and HG groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the ROC curve analysis performed for the discriminatory power of SII, and PIV to predict a pregnancy in which NVP will develop, the AUC values were 0.685 and 0.564, respectively. The cut-off values for SII, and PIV were \u0026gt;\u0026thinsp;1207x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L (47.48% sensitivity, 82.01% specificity) and \u0026gt;\u0026thinsp;783x10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L\u003csup\u003e2\u003c/sup\u003e (27.34% sensitivity, 86.33% specificity), respectively. In addition, ROC curve analysis for discriminatory power of SII, SIRI and PIV to predict pregnancy with NVP requiring hospitalization yielded AUC values of 0.639, 0.625 and 0.627, respectively. The cut-off values for SII, SIRI and PIV were \u0026gt;\u0026thinsp;1056x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L (64.55% sensitivity, 68.97% specificity), 32.12x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003csup\u003e2\u003c/sup\u003e (77.27% sensitivity, 43.10% specificity), and \u0026gt;\u0026thinsp;350x10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L (77.27% sensitivity, 46.55% specificity), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Comparisons of the ROC curves of these indices for predicting NVP and inpatient treatment needs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC curve analysis for various parameters derived from the CBC that can be used to predict NVP and the need for inpatient treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e+ LHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e- LHR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNVP prediction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSII (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.644\u0026thinsp;\u0026minus;\u0026thinsp;0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePIV (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.522\u0026ndash;0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eInpatient treatment Prediction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSII (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.580\u0026ndash;0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSIRI (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.565\u0026ndash;0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePIV (10\u003c/b\u003e\u003csup\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e/\u0026micro;L\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.568\u0026ndash;0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;350.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e77.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eAUC, area under the curve; CBC, complete blood count; CI, confidence interval; LHR, likelihood ratio; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; ROC, receiver operating characteristic; SII, systemic immune-inflammatory index; SIRI, systemic inflammation response index; \u0026micro;L, microliter.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eA p value of \u0026lt;\u0026thinsp;0.05 indicates a significant difference. Statistically significant p-values are in bold.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe comparison of overall performance in predicting NVP development showed that SII was better than PIV (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while there was no significant superiority between SII, SIRI and PIV in predicting inpatient treatment needs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CBC-derived inflammatory indices that can be used to predict NVP and the need for inpatient treatment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNVP prediction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eInpatient treatment prediction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePIV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAUC, area under the curve; NVP, nausea and vomiting in pregnancy; PIV, pan-immune inflammation value; SII, systemic immune-inflammatory index; SIRI, systemic inflammation response index.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eA p value of \u0026lt;\u0026thinsp;0.05 indicates a significant difference. Statistically significant p-values are in bold. For comparisons where the p-value is significant, the parameter with the higher AUC value in\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eis superior in prediction.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWhile mild NVP is a common pregnancy condition, especially in the first trimester, the etiopathologic mechanism leading to severe NVP, also known as HG, is not fully understood and several factors are thought to play a role in the etiology [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The risk of HG is increased by known risk factors such as multiple pregnancies, molar pregnancies and pre-pregnancy risk factors such as underweight, primiparity, hyperthyroidism, asthma, motion sickness and gastrointestinal disorders [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, current knowledge of the other risk factors is limited as the studies investigating HG generally have a small sample size, leading to inconclusive results. In this respect, HG is very well defined by a recently published study with a large sample size over a long period of time. It concludes that pregnancies diagnosed with HG differ not only from pregnancies of women who never diagnosed with HG, but also from HG patients\u0026rsquo; other, non-hyperemesis gravidarum pregnancies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrediction and early diagnosis of HG is crucial to avoid serious maternal, fetal and neonatal consequences such as vitamin B1 and K deficiency, Wernicke's encephalopathy, dehydration, electrolyte imbalance, malnutrition, preterm birth, fetal growth restriction and yet unknown consequences for the offspring [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This is all the more true for pregnant women in rural areas who live far away from health centers and do not have the means to visit them frequently enough, as well as for women who do not seek medical care due to cultural and financial constraints [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough several studies have been conducted with biomarkers such as leptin, nesfatine-1, ghrelin, obestatin and prealbumin, their role in predicting HG is unclear due to the inconsistent results and small sample size of the studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, there is currently no generally accepted tool in the clinical setting that can accurately predict the development of HG. Leptin levels in women with HG compared to women in the control group were found to be higher, similar or lower in various studies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similarly, nisfatin-1 levels were higher in women with HG compared to women in the control group in the Gungor et al. study, while Ozturk et al. found no difference [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. On the other hand, some studies found that beta-human chorionic gonadotropin (beta-hCG), thyroxine and its free form were elevated in pregnancies with HG, indicating a strong association between these variables and HG [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The sample sizes in all these studies were not large enough and BMI-adjusted values could not be compared because no data on the patients' BMI measurements were available. In addition to the biomarkers, ultrasound markers were also investigated for HG prediction. A study by Yıldız et al. found that placental thickness and free beta-hCG levels were higher in patients with HG [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is already known that a high beta-hCG level is associated with HG. Since placental mass correlates with beta-hCG levels, it is obvious that placental mass is indirectly related to HG. However, accurate measurement of placental thickness may not be possible depending on many factors, including subcutaneous adipose tissue thickness, placental localization, intra- and interobserver variability, and the difficulty of standardizing the part to be measured.\u003c/p\u003e \u003cp\u003eAs inflammatory markers derived from a single CBC have gained attention in recent years and the search for inexpensive, widely available, easily applicable and interpretable biomarkers that can more accurately predict the development, prognosis and/or severity of diseases with altered inflammation such as HG continues, we conducted this article. Furthermore, to our knowledge, this article is the first study to investigate SIRI and PIV as predictive markers for predicting women who develop NVP and its severity. As mentioned in the introduction, the hypothesis of the current study was based on altered inflammation, which seems to play an important role in the development of HG. Inflammatory indices and cell lines derived from the CBC have been the subject of research for NVP. In the study by Tayfur et al., the neutrophil, lymphocyte and platelet counts, NLR, PLR and plateletcrit differed significantly between the HG and control groups [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, as altered inflammation is a complex process, it would not be realistic, repeatable or meaningful to make decisions based on a single cell line such as neutrophils, lymphocytes or platelets alone. In this context, we investigated SII, SIRI and PIV, which collectively assess different individual inflammatory cell lines and have previously been studied for the prediction and prognosis of many diseases in which inflammation plays a role in pathogenesis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, in the above-mentioned study, all women with NVP of varying severity were grouped under the term 'HG', which should only refer to patients with severe NVP as defined in the same study [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, the range of gestational age of the participants when the CBC data were collected in the study was quite wide (before 22 weeks gestation) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Since the composition of the CBC parameters varies with increasing gestational age, the inclusion of pregnant women with a wide range of gestational ages leads to less consistency in the results.\u003c/p\u003e \u003cp\u003eIn line with the literature, the results of our study showed a higher BMI, higher gravidity and higher parity in patients who did not have NVP. Higher BMI is associated with lower NVP rates, and since previous NVP is a significant risk factor for recurrence of NVP, it is not surprising that women who have experienced NVP and HG are less likely to want to become pregnant again [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Main findings of the present study were: (1) SII and PIV were significantly higher in the NVP group than in the control group. An SII with a cut-off value of \u0026gt;\u0026thinsp;1207x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L had a higher discriminatory power in predicting NVP than a PIV with a cut-off value of \u0026gt;\u0026thinsp;783x10\u003csup\u003e6\u003c/sup\u003e/\u0026micro;L\u003csup\u003e2\u003c/sup\u003e. (2) SII, SIRI and PIV were significantly higher in both the moderate and severe NVP groups than in the mild NVP group, while there were no significant differences between the moderate NVP and HG groups. However, distinguishing who will develop moderate or severe NVP (HG) in patients predicted to have NVP does not contribute to clinical practice. For this reason, although their predictive power is the same, we can use SII, SIRI and PIV to differentiate between patients with mild NVP and patients with moderate and severe NVP, who almost always require hospitalization.\u003c/p\u003e \u003cp\u003eAlthough these calculations seem complicated, the results can be integrated into the CBC reports using software programming. Our study has some limitations, mainly due to its retrospective nature. Major strength of this study is that it was conducted in a large tertiary referral hospital using the same algorithms for diagnosis, treatment and follow-up. Furthermore, since all participants were randomly assigned to the control group according to the respective time of hospital admission after each patient with NVP, the impact of bias on the results was very small despite the retrospective nature of the study.\u003c/p\u003e \u003cp\u003eIn conclusion, our results showed an association between high SII and PIV and an increased risk of NVP. In addition, distinguishing between patients with mild and moderate and/or severe NVP with SII, SIRI and PIV can facilitate decision making in treatment planning, as patients in the latter group are at high risk of morbidity and almost always require inpatient treatment. Further prospective, randomized, controlled studies are needed to better define the efficacy and limitations of these markers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eML Dereli: Study design, project development, analysis strategy, manuscript writing\u003c/p\u003e\n\u003cp\u003eS \u0026Ouml;zkan: Project development, data analysis\u003c/p\u003e\n\u003cp\u003eB Savran \u0026Uuml;\u0026ccedil;ok: Data collection\u003c/p\u003e\n\u003cp\u003eS Topkara: Statistical analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003eS Sucu: Statistical analysis, manuscript writing\u003c/p\u003e\n\u003cp\u003eFB Fıratlıgil: Statistical analysis, literature search\u003c/p\u003e\n\u003cp\u003eD Kurt: Data collection\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Kurt: Data collection\u003c/p\u003e\n\u003cp\u003eŞ \u0026Ccedil;elen: Proofreading, language support\u003c/p\u003e\n\u003cp\u003eY Engin \u0026Uuml;st\u0026uuml;n: Proofreading, language support\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATEMENTS AND DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University of Health Sciences, Ankara Etlik Lady Z\u0026uuml;beyde Training and Research Hospital, Ankara, Turkey (Date:22.02.2022, No:03/26). The study was conducted at the Department of Obstetrics and Gynecology, Ankara Etlik Lady Z\u0026uuml;beyde Training and Research Hospital, Ankara, Turkey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate and publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of University of Health Sciences, Ankara Etlik Lady Z\u0026uuml;beyde Training and Research Hospital, Ankara, Turkey waived the requirement to obtain informed consent because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in \u0026ldquo;figshare\u0026rdquo; at http://doi.org/10.6084/m9.figshare.24996407\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoelig RC, Barton SJ, Saccone G, Kelly AJ, Edwards SJ, Berghella V (2018) Interventions for treating hyperemesis gravidarum: a Cochrane systematic review and meta-analysis. 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Acta Obstet Gynecol Scand 102(9):1176\u0026ndash;1182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aogs.14629\u003c/span\u003e\u003cspan address=\"10.1111/aogs.14629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Complete blood count, hemogram, hyperemesis gravidarum, index, pan-immune value, severity","lastPublishedDoi":"10.21203/rs.3.rs-4013479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4013479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo investigate the role of the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI) and pan-immune inflammation value (PIV) in predicting nausea and vomiting in pregnancy (NVP)\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWomen diagnosed and managed for NVP at a large tertiary hospital between 2016 and 2021 were retrospectively analyzed. After applying the inclusion criteria, a total of 278 eligible patients with NVP and 278 gestational age-matched healthy pregnant women were included. Patients with NVP were divided into mild (n\u0026thinsp;=\u0026thinsp;58), moderate (n\u0026thinsp;=\u0026thinsp;140) and severe NVP (n\u0026thinsp;=\u0026thinsp;80). Patients with moderate and/or severe NVP who were at high risk for hospitalization were pooled and assigned to an inpatient treatment group. The groups were then compared.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSII and PIV were significantly higher in the NVP group than in the control group, while SII, SIRI and PIV were significantly higher in the inpatient treatment group than in the mild NVP group. The comparison of overall performance in predicting NVP development showed that SII was better than PIV (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while there was no significant superiority between SII, SIRI and PIV in predicting inpatient treatment needs. An SII with a cut-off value of \u0026gt;\u0026thinsp;1207x10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L (47.48% sensitivity, 82.01% specificity) had the highest discriminatory power for predicting a pregnancy in which NVP will develop.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results suggest an association between high SII and PIV and an increased risk of future NVP. These markers can be used as a first-trimester screening test to improve treatment planning of pregnancies at high risk of HG.\u003c/p\u003e","manuscriptTitle":"The role of systemic inflammatory indices in predicting nausea and vomiting in pregnancy and the need for hospitalization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:31:58","doi":"10.21203/rs.3.rs-4013479/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29e1604d-95d9-4e1e-b0e5-8b9dcfe3d9a6","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-26T12:06:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:31:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4013479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013479","identity":"rs-4013479","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}