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Placental Alpha-Microglobulin-1 Test in Resource-Limited Settings

A Cost-Effectiveness Analysis

Echebiri, Nelson C. MD, MBA; Sinkey, Rachel G. MD; Szczepanski, Jamie L. MD; Shelton, James A. MS; McDoom, M. Maya PhD, MPH; Odibo, Anthony O. MD

doi: 10.1097/AOG.0000000000001258
Contents: Original Research
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OBJECTIVE: To evaluate whether the use of placental alpha-microglobulin-1 (PAMG-1) for the diagnosis of preterm premature rupture of membranes is cost-effective in resource-limited settings.

METHODS: We designed a decision-analytic model from a third-party payer's perspective to determine the cost-effectiveness of the PAMG-1 test compared with the traditional diagnostic test of pooling, Nitrazine, and ferning in diagnosing preterm premature rupture of membranes in a resource-limited setting. The primary health outcome of interest is the number of hospital transfers averted by each strategy per 1,000 patients screened. Baseline probabilities and cost assumptions were derived from published literature. We conducted sensitivity analyses using both deterministic and probabilistic models. Cost estimates reflect 2015 U.S. dollars.

RESULTS: Under our baseline parameters, the use of a PAMG-1 test was the preferred cost-effective strategy. The PAMG-1 test averted hospital transfers of 447 true-negative patients per 1,000 tested at a cost of $143,407 ($320.82 per hospital transfer averted). The traditional test averted hospital transfers of 395 true-negative patients per 1,000 tested at a cost of $172,652 ($437.40 per hospital transfer averted). In a Monte Carlo simulation of 10 million trials, the PAMG-1 test was selected as the most cost-effective strategy with a frequency of 74%. The traditional test was only selected with a frequency of 26%. The “do-nothing” strategy was not selected throughout the trial.

CONCLUSION: Among women presenting at resource-limited settings with a history suspicious of preterm premature rupture of membranes between 24 and 36 weeks of gestation, our analysis provides evidence suggesting that PAMG-1 is the most cost-effective testing strategy.

In the evaluation of preterm premature rupture of membranes, placental alpha-microglobulin-1 is the most cost-effective test strategy in resource-limited settings.

Department of Obstetrics and Gynecology, Meritus Health, Hagerstown, Maryland; the Department of Obstetrics and Gynecology, University of South Florida, Tampa, Florida; the Department of Obstetrics and Gynecology, University at Buffalo, Buffalo, New York; the Social Science Research Center, Mississippi State University, Starkville, Mississippi; and the Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.

Corresponding author: Nelson C. Echebiri, MD, MBA, Department of Obstetrics and Gynecology, Meritus Health, 11110 Medical Campus Road Suite 249, Hagerstown, MD 21742; e-mail: nelsonechebiri@gmail.com.

Financial Disclosure The authors did not report any potential conflicts of interest.

Preterm premature rupture of membranes (PROM) complicates 3% of pregnancies and accounts for 20–40% of PROM.1 Given that preterm PROM causes approximately one third of preterm deliveries in the United States,2 accurate and timely diagnosis is important. Most resource-limited hospitals are not equipped to provide obstetric or neonatal medical needs. Therefore, these facilities transfer ruptured patients to a higher level of care. False diagnosis can delay implementation of salutary obstetric measures1,3 or lead to unnecessary and substantial costs associated with hospital transfers.

Traditionally, the diagnosis of preterm PROM relies on the health care provider's ability to perform a sterile speculum examination for pooling, ferning, Nitrazine testing, or all of these.4 In resource-limited settings, trained obstetric personnel may not be available to perform a speculum examination and evaluate patients who report leakage of fluid. However, placental alpha-microglobulin-1 (PAMG-1) has emerged as a biomarker in the diagnosis of preterm PROM.1,3–8 Unlike the traditional test that required a speculum examination, PAMG-1 does not require a speculum or experienced obstetric personnel.

The PAMG-1 test is marketed as AmniSure at a reimbursement cost of $85.42 per test, whereas the cost of the traditional test is $14.16.9 Furthermore, the cost of a single patient transfer ranges from $800 to $8,800.10–12 Therefore, the question of whether to use PAMG-1 or the traditional test in resource-limited settings should be guided by the cost-effectiveness of each test. Using decision-analytic modeling, we evaluated the cost-effectiveness of PAMG-1 in a theoretical cohort of women presenting with histories suspicious for preterm PROM at resource-limited settings.

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MATERIALS AND METHODS

Using decision-analytic modeling techniques described by Plevritis,13 Detsky et al,14,15 Naglie et al,16 and Krahn et al,17 we designed a cost-effectiveness analysis. The decision tree consisted of two preterm PROM testing strategies: PAMG-1 and the traditional diagnostic tests (Fig. 1). We compared these two testing strategies with “a no test, transfer all patients” strategy. The decision tree diagram and all computations were performed using commercially available decision analysis software (TreeAge Pro Suite 2015 Software, Inc). The model was populated with 1,000 hypothetical patients presenting with preterm PROM. Because no human subjects were involved and we used only a collection of publicly available and de-identified data sources, this study was considered exempt from institutional review board review.

Fig. 1

Fig. 1

Our measure of effectiveness is the number of hospital transfers averted by each strategy per 1,000 patients screened. We utilized the specificity of each test to provide an index of test performance in effectively averting hospital transfers by accurately classifying a patient as true-negative for preterm PROM. We estimated that the prevalence of preterm PROM in patients presenting with a history, signs, and symptoms of ruptured fetal membranes is approximately 30% based on existing literature.1,3,4,8 We assumed a preterm PROM range of 10–40% for sensitivity analysis. Bayes' theorem of conditional probabilities was then used to calculate the probability of a positive test (sensitivity×prevalence)+[(1−specificity)×(1−prevalence)] irrespective of the membrane status. With this baseline prevalence and range, we calculated the effectiveness of each test in averting hospital transfer (specificity×[population−{prevalence×population}]) as shown in Table 1. For the sake of the analysis, we assumed that preterm patients with positive results from any of the strategies were transferred and that preterm patients with negative results were discharged home.

Table 1

Table 1

We conducted sensitivity analyses to evaluate the uncertainty of the results. Both probability and cost parameters were varied to take into account potential clinical scenarios that might deviate from our baseline estimates. One-way deterministic sensitivity analyses were performed using a tornado diagram to obtain the most influential variables. We then conducted a two-way deterministic sensitivity analyses of the influential variables derived from the tornado diagram. Finally, a probabilistic sensitivity analysis was performed with Monte Carlo simulation to evaluate the robustness of our findings by simultaneously varying all these variables. Monte Carlo simulation samples values from the distribution defined in the tree and recalculates expected values based on each parameter sample. In this simulation, we sent a cohort of 1,000 patients through the model 10,000 times for a total of 10 million trials. The distribution used in the Monte Carlo simulation for Current Procedural Terminology–related costs was normal. Gamma distributions were used for other costs based on rough estimates, and β distributions were used for probabilities. We calculated all distributions using the range for each variable as shown in Table 1.

In addition, comparative summary measures including cost per strategy and number of hospital transfers averted per test were calculated. Dominated strategies that were less effective but more costly than other competing strategies were identified. We calculated cost-effectiveness ratios and incremental cost-effectiveness ratios. The incremental cost-effectiveness ratio is the additional cost divided by its additional benefit of moving from a strategy of “do nothing and transfer all” to using the traditional test or PAMG-1.

To obtain the model inputs, we performed literature searches on PubMed and MEDLINE using the following terms: AmniSure; PAMG-1; preterm PROM; traditional and clinical method of preterm PROM diagnosis; prevalence of preterm PROM; cost analysis, cost-effectiveness, and combinations of these terms. We also reviewed the reference lists from retrieved articles to identify additional publications. The broad literature search ensured that no single source or type of publication provided the majority of the data elements utilized. When available, we used published meta-analyses to derive probability estimates. The 2015 fee schedules with Current Procedural Terminology codes were obtained from the Centers for Medicare & Medicaid Services where applicable.9–12 The baseline assumptions, probabilities, and cost values used represented the best available estimates from the literature and are summarized with their references in Table 1. Where published base case values were not available, the distributions in Table 1 were used to derive the mean for the baseline analysis.

All cost estimates were reported in 2015 U.S. dollars. Annual discounting was not necessary because the time horizon was within 1 year. In the absence of an a priori established cost-effectiveness threshold for the prevention of false maternal transfers for preterm PROM, we used $50,000 as our threshold. Therefore, the analysis was done from the third-party payers' perspective using a cost-effectiveness threshold of $50,000 per hospital transfer averted. The only relevant cost was the upfront costs (cost of test) to avert hospital transfer. We defined direct costs as Centers for Medicare & Medicaid Services reimbursement for the test or services provided. Indirect costs (lost time and wages; cost of support services of families away from home) associated with the diagnosis of preterm PROM are difficult to quantify and were not included in this analysis. The Centers for Medicare & Medicaid Services reimbursement cost of PAMG-1, Nitrazine, and ferning is provided in Table 1. The reimbursement cost of PAMG-1, Nitrazine, and ferning reflects the weighted average of all 50 states across the United States. We assumed the cost burden of a false-positive result as the cost of unnecessary hospital transfer through ground or air transportation. Data are lacking on the cost of discharging patients with false-negative results home. Patients who are discharged based on false-negative results of preterm PROM are at an increased risk of infection (early-onset neonatal sepsis, septic shock, pneumonia, and meningitis), intraventricular hemorrhage, cord accident, preterm delivery, and perinatal death.22–24 Therefore, to estimate the cost burden of discharging false-negative patients home, we accounted for three simplified scenarios: the probability of resealing of membranes; the probability of persistent leakage of fluid leading to infection and perinatal death; and the probability of persistent leakage of fluid without any complications. We used the cost of perinatal death as a surrogate for the cost burden of a false-negative result. In 2003, Phibbs et al25 published that the cost of perinatal death ranged from $44,480 to $161,210. This is the cost of perinatal death between 24 and 36 weeks of gestation from hospitalization at birth and all in care before death. After adjusting the 2003 estimates for inflation, we used a range of $57,486 to $208,350 and the average, $132,918, for baseline analysis. In 1998, Hook et al10 estimated that the cost of transfer of an obstetric patient by air transport was $4,613.64±$581.12, whereas ground transportation was $604.02±$306.38. Using Centers for Medicare & Medicaid Services and published literature, we estimated that the cost of ground transportation would range from $500 to $1,600,10–12 whereas the cost of air transportation ranged from $5,400 to $8,800.10–12 We assumed that most patients will be transported by ground transportation. We used $1,000 for baseline analysis as the average cost of hospital transfer. We calculated the total cost for each strategy: (cost of the test+transfer cost [if transferred])×membrane status (ruptured or not ruptured)×the probability of true-positive, true-negative, false-positive, or false-negative where applicable.

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RESULTS

In the cohorts of 1,000 pregnant patients who entered the model with a history suspicious of preterm PROM, the model favors the use of PAMG-1 as the most cost-effective strategy compared with the traditional method (Table 2). Transferring all patients without testing was not considered a viable strategy because it was significantly expensive. The significant and unnecessary cost associated with a “do nothing strategy” was mainly driven by the cost of transportation. The PAMG-1 test averted hospital transfers of 447 true-negative patients per 1,000 tested at a cost of $143,407 ($320.82 per hospital transfer averted). The use of a PAMG-1 test in the diagnosis of preterm PROM was also associated with cost savings. The cost saved by using a PAMG-1 test instead of the traditional test was $$564.34 per transfer averted. The traditional test averted hospital transfers of 395 true-negative patients per 1,000 tested at a cost of $172,652 ($437.40 per hospital transfer averted). The cost difference between the traditional test strategy and PAMG-1 test strategy was mainly driven by the economic burden of discharging a patient with a false-negative result home.

Table 2

Table 2

Using the cost-effectiveness threshold of $50,000, Monte Carlo probabilistic sensitivity analysis showed that in 10 million trials, the PAMG-1 test was selected as the optimal cost-effective strategy with a frequency of 74% (Fig. 2). The traditional test was only selected with a frequency of 26%. The “do nothing strategy” was not selected throughout the trial. The simulation showed that the traditional test had an average effectiveness of 393 with a minimum of 158 and a maximum of 687. It had an average cost of $170,830 with a minimum of $9,460 and a maximum cost of $3,196,163 per strategy. The PAMG-1 test had an average effectiveness of 446 with a minimum of 214 and a maximum of 780. It had an average cost of $146,011 with minimum of $41,872 and a maximum cost of $2,799,585 per strategy (not shown).

Fig. 2

Fig. 2

A tornado diagram of the univariate sensitivity analyses showed the most influential variables and the degree to which uncertainty in each variable affect the incremental cost-effectiveness ratio (Fig. 3). Each bar in this diagram represents the effect of an individual variable on the incremental cost-effectiveness ratio as that value is varied across the indicated range while other input variables are held constant. The variables that elicit the most influence on the incremental cost-effectiveness ratio are the probability of a positive traditional test irrespective of membrane status, the specificity and sensitivity of the traditional test, the cost of transfer, and the cost of a false-negative test. The model parameters least likely to have an effect on the incremental cost-effectiveness ratio are the effectiveness of both PAMG-1 and the traditional test in averting hospital transfers.

Fig. 3

Fig. 3

Appendices 1–4, available online at http://links.lww.com/AOG/A764, http://links.lww.com/AOG/A765, http://links.lww.com/AOG/A766, and http://links.lww.com/AOG/A767, respectively, are the sensitivity analyses of each of the aforementioned influential variables. Appendices 1 and 2 (http://links.lww.com/AOG/A764 and http://links.lww.com/AOG/A765, respectively) are two-way deterministic sensitivity analyses of the probability of a positive traditional test in comparison with the probability of a positive PAMG-1 test irrespective of membrane status and the specificity of the traditional test in comparison with the specificity of PAMG-1 test. In both figures, PAMG-1 test remained the preferred strategy at each corresponding probability estimate. Appendices 3–4 (http://links.lww.com/AOG/A766 and http://links.lww.com/AOG/A767, respectively) are one-way sensitivity analyses of the cost of transfer and the cost of a false-negative test, respectively. At a transfer cost of $800–$2,800, the PAMG-1 test was the dominant and preferred strategy with cost savings of $723 (incremental cost-effectiveness ratio). Above $2,800, the traditional test was found to be cost-effective but not cost-saving in comparison with PAMG-1. If the economic burden of a false-negative test is $57,486 or less, the traditional test was cost-effective but not cost-saving in comparison with the PAMG-1 test. As the cost of a false-negative test increased to $95,202 and beyond, the PAMG-1 test was found to be both cost-effective and cost-saving.

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DISCUSSION

This model assessed the economic implications of different test strategies in the diagnosis of preterm PROM among patients presenting at resource-limited settings between the gestational ages of 24 and 36 weeks. Use of PAMG-1 was the most cost-effective strategy in averting hospital transfer from a resource-limited setting to a higher level of care. The savings derived from PAMG-1 are dependent on its higher sensitivity and specificity. Fewer women with ruptured membranes are discharged home by virtue of its higher sensitivity; those discharged home are more likely to have intact membranes. In addition, the intangible cost saved by PAMG-1 could not be captured by the tree but the ease of using it is an added benefit that could be invaluable in low-resource areas.

Studies have suggested the economic benefit of using PAMG-1 in comparison with clinical evaluation. Birkenmaier et al4 evaluated the performance of PAMG-1 in patients with uncertain rupture of membranes in a prospective cohort study and concluded that PAMG-1 was more sensitive compared with clinical assessment. They suggest that PAMG-1 could be cost-effective, although a cost-effectiveness analysis was not performed. Recently, Echebiri et al26 concluded that PAMG-1 is cost-beneficial in the diagnosis of nonobvious cases of preterm PROM. Our current study is a cost-effectiveness analysis of PAMG-1 in resource-limited settings and differs from the aforementioned studies. We used a cost-effectiveness model to provide the incremental cost-effectiveness ratio by analyzing the cost and the clinical effectiveness of both PAMG-1 and the traditional test.

Although the appropriate threshold for cost-effectiveness analysis remains an ongoing debate, interventions in the United States that yield a cost-effectiveness ratio of less than $50,000–$100,000 per quality-adjusted life-year gained are considered acceptable and cost-effective.27 Therefore, PAMG-1 and the traditional test are found to be cost-effective using the same economic threshold. However, using the same $50,000 threshold per hospital admission averted should be interpreted with caution because there is no a priori established cost-effectiveness threshold for averting hospital admission that could potentially lead to unnecessary interventions and iatrogenic preterm delivery. Considering the societal economic burden of a false diagnosis leading to complications or iatrogenic preterm delivery, we would expect high cost-effectiveness thresholds for averting unnecessary hospital transfers that could lead to preterm delivery.

The model was most sensitive to the probability of a positive traditional test irrespective of membrane status. The probability of a positive test irrespective of disease status is dependent on the sensitivity of the test and the prevalence of the disease. Given that PAMG-1 already has higher sensitivity than the traditional test, the probability of a positive traditional test will correspond to a similar or higher probability of a positive PAMG-1 test. Therefore, PAMG-1 will remain cost-effective at any given probability of a positive traditional test. The model was also sensitive to the cost of a false-negative test. As expected, the test with better sensitivity and specificity is preferred when the economic burden of a false-negative test is high. Although the model was also sensitive to the cost of transfer, the traditional test was marginally preferred at a high cost of transfer. This supports anecdotal evidence, which expects the test with a higher sensitivity to detect more true-positive preterm PROM.

There are several limitations of the model. First, every decision-analytic economic evaluation has limitations that are dependent on the data used in the formulation of the model. Such limitations are applicable to our study. Second, reasonable assumptions and rough cost estimates without precision were made. The cost of transfer or the economic burden of a false-negative test could have been over- or understated. Indirect patient and family costs associated with transfer were also not accounted for in this model. Third, there are no published data on the effectiveness of PAMG-1 or the traditional test in averting hospital transfers. We used the specificity of each test as an index of test performance in effectively averting hospital admission by accurately classifying a patient as true-negative for preterm PROM. Using a different test characteristic as a surrogate for each test's effectiveness in averting hospital admissions might change the overall results. However, sensitivity analyses showed that the model was not sensitive to the effectiveness of each test.

Despite these limitations, the major strength of the study is in its design, timeliness, and clinical relevance to a common clinical presentation in resource-limited settings. Our assumptions and base variables were mitigated by an exhaustive literature search. We addressed these uncertainties by both deterministic and probabilistic sensitivity analyses. The outcome of the analyses was very robust.

Cost-effectiveness analyses are only one dimension of medical decision-making. However, it uses test accuracy, clinical effect, and costs to establish if the new, often more expensive modality offers increased value for the expected clinical outcome.28 Findings from this analysis have shown that the PAMG-1 test is the most cost-effective point-of-care test in resource-limited settings for preterm PROM diagnoses.

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REFERENCES

1. Cousins LM, Smok DP, Lovett SM, Poeltler DM. AmniSure placental alpha microglobulin-1 rapid immunoassay versus standard diagnostic methods for detection of rupture of membranes. Am J Perinatol 2005;22:317–20.
2. Mercer BM. Preterm premature rupture of the membranes: current approaches to evaluation and management. Obstet Gynecol Clin North Am 2005;32:411–28.
3. Albayrak M, Ozdemir I, Koc O, Ankarali H, Ozen O. Comparison of the diagnostic efficacy of the two rapid bedside immunoassays and combined clinical conventional diagnosis in prelabour rupture of membranes. Eur J Obstet Gynecol Reprod Biol 2011;158:179–82.
4. Birkenmaier A, Ries JJ, Kuhle J, Burki N, Lapaire O, Hösli I. Placental α-microglobulin-1 to detect uncertain rupture of membranes in a European cohort of pregnancies. Arch Gynecol Obstet 2012;285:21–5.
5. Sosa CG, Herrera E, Restrepo JC, Strauss A, Alonso J. Comparison of placental alpha microglobulin-1 in vaginal fluid with intra-amniotic injection of indigo carmine for the diagnosis of rupture of membranes. J Perinat Med 2014;42:611–6.
6. Lee SE, Park JS, Norwitz ER, Kim KW, Park HS, Jun JK. Measurement of placental alpha-microglobulin-1 in cervicovaginal discharge to diagnose rupture of membranes. Obstet Gynecol 2007;109:634–40.
7. Ramsauer B, Vidaeff AC, Hosli I, Park JS, Strauss A, Khodjaeva Z, et al.. The diagnosis of rupture of fetal membranes (ROM): a meta-analysis. J Perinat Med 2013;41:233–40.
8. Tagore S, Kwek K. Comparative analysis of insulin-like growth factor binding protein-1 (IGFBP-1), placental alpha-microglobulin-1 (PAMG-1) and nitrazine test to diagnose premature rupture of membranes in pregnancy. J Perinat Med 2010;38:609–12.
9. Centers for Medicare & Medicaid Services. Clinical laboratory fee schedule. Available at: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/ClinicalLabFeeSched/clinlab.html. Retrieved July 12, 2015.
10. Van Hook JW, Leicht TG, Van Hook CL, Dick PL, Hankins GD, Harvey CJ. Aeromedical transfer of preterm labor patients. Tex Med 1998;94:88–90.
11. Delgado MK, Staudenmayer KL, Wang NE, Spain DA, Weir S, Owens DK, et al.. Cost-effectiveness of helicopter versus ground emergency medical services for trauma scene transport in the United States. Ann Emerg Med 2013;62:351–364.e19.
12. Centers for Medicare & Medicaid Services. Ambulance fee schedule public use files. Available at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AmbulanceFeeSchedule/afspuf.html. Retrieved July 12, 2015.
13. Plevritis SK. Decision analysis and simulation modeling for evaluating diagnostic tests on the basis of patient outcomes. AJR Am J Roentgenol 2005;185:581–90.
14. Detsky AS, Naglie G, Krahn MD, Naimark D, Redelmeier DA. Primer on medical decision analysis: part 1—getting started. Med Decis Making 1997;17:123–5.
15. Detsky AS, Naglie G, Krahn MD, Redelmeier DA, Naimark D. Primer on medical decision analysis: part 2—building a tree. Med Decis Making 1997;17:126–35.
16. Naglie G, Krahn MD, Naimark D, Redelmeier DA, Detsky AS. Primer on medical decision analysis: part 3—estimating probabilities and utilities. Med Decis Making 1997;17:136–41.
17. Krahn MD, Naglie G, Naimark D, Redelmeier DA, Detsky AS. Primer on medical decision analysis: part 4—analyzing the model and interpreting the results. Med Decis Making 1997;17:142–51.
18. El-Messidi A, Cameron A. Diagnosis of premature rupture of membranes: inspiration from the past and insights for the future. J Obstet Gynaecol Can 2010;32:561–9.
19. Ng BK, Lim PS, Shafiee MN, Ghani NA, Ismail NA, Omar MH, et al.. Comparison between AmniSure placental alpha microglobulin-1 rapid immunoassay and standard diagnostic methods for detection of rupture of membranes. Biomed Res Int 2013;2013:587438.
    20. Eleje GU, Ezugwu EC, Ogunyemi D, Eleje LI, Ikechebelu JI, Igwegbe AO, et al.. Accuracy and cost-analysis of placental alpha-microglobulin-1 test in the diagnosis of premature rupture of fetal membranes in resource-limited community settings. J Obstet Gynaecol Res 2015;41:29–38.
    21. Johnson JW, Egerman RS, Moorhead J. Cases with ruptured membranes that “reseal”. Am J Obstet Gynecol 1990;163:1024–30.
    22. Yu H, Wang X, Gao H, You Y, Xing A. Perinatal outcomes of pregnancies complicated by preterm premature rupture of the membranes before 34 weeks of gestation in a tertiary center in China: a retrospective review. Biosci Trends 2015;9:35–41.
    23. Morales WJ, Washington SR 3rd, Lazar AJ. The effect of chorioamnionitis on perinatal outcome in preterm gestation. J Perinatol 1987;7:105–10.
    24. Soraisham AS, Singhal N, McMillan DD, Sauve RS, Lee SK; Canadian Neonatal Network. A multicenter study on the clinical outcome of chorioamnionitis in preterm infants. Am J Obstet Gynecol 2009;200:372.e1–6.
    25. Phibbs CS, Schmitt SK. Estimates of the cost and length of stay changes that can be attributed to one-week increases in gestational age for premature infants. Early Hum Dev 2006;82:85–95.
    26. Echebiri NC, McDoom MM, Pullen JA, Aalto MM, Patel NN, Doyle NM. Placental alpha-microglobulin-1 and combined traditional diagnostic test: a cost-benefit analysis. Am J Obstet Gynecol 2015;212:77.e1–10.
    27. Marseille E, Larson B, Kazi DS, Kahn JG, Rosen S. Thresholds for the cost-effectiveness of interventions: alternative approaches. Bull World Health Organ 2015;93:118–24.
    28. Drain PK, Hyle EP, Noubary F, Freedberg KA, Wilson D, Bishai WR, et al.. Diagnostic point-of-care tests in resource-limited settings. Lancet Infect Dis 2014;14:239–49.

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