Journal of Sleep Medicine and Disorders

Diagnostic Accuracy of a Mathematical Model to Predict Apnea-Hypopnea Index using Nighttime Pulse Oximetry

Short Communication | Open Access

  • 1. Department of Neurology, Weill Cornell Medical College, USA
  • 2. Department of Medicine, Weill Cornell Medical College, USA
+ Show More - Show Less
Corresponding Authors
Matthew R. Ebben, Department of Neurology, Weill Cornell Medical College, Center for Sleep Medicine, 425 East 61st Street, 5th Floor, New York, N.Y. 10065, USA

Purpose: The intent of this study is to develop a predictive model to convert an oxygen desaturation index (ODI) to an apnea and hypopnea index (AHI). This model will then be compared to actual AHI to determine its precision.

Methods: 1467 subjects given polysomnograms with concurrent pulse oximetry between April 14, 2010 and February 7, 2012, were divided into model development (n=733) and verification groups (n=734) in order to develop a predictive model of AHI using ODI. Quadratic regression was used for model development.

Results: The coefficient of determination (r2 ) between the actual AHI and the predicted AHI (PredAHI) was 0.80 (r=0.90), which was significant at a p<0.001. The areas under the ROC curve ranged from 0.96 for AHI thresholds of ≥10 and ≥15/hr. to 0.97 for thresholds of ≥5 and ≥30/hr.

Conclusions: The algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea.


Ebben MR, Krieger AC (2016) Diagnostic Accuracy of a Mathematical Model to Predict Apnea-Hypopnea Index using Nighttime Pulse Oximetry. J Sleep Med Disord 3(1): 1040.


•    Oxygen desaturation index
•    Sleep apnea
•    Apnea-hypopnea index
•    Sensitivity
•    Specificity


Obstructive sleep apnea (OSA) is a condition that involves multiple episodes of airway closure and/or reduction in airflow that affects 2-4% of the population [1]. OSA has been associated with adverse medical conditions including congestive heart failure [2], stroke [3], pulmonary [4] and systemic [5] hypertension, cancer [6], and increased mortality [7]. Traditionally, sleep apnea has been assessed via a nighttime polysomnogram. This diagnostic procedure continues to be the gold standard for the evaluation of sleep apnea.

Numerous studies have looked at the role of pulse oximetry in the assessment of OSA [8-14]. These previous investigations have developed several methods for predicting AHI from oximetry data, these include: cumulative time below an oxyhemoglobin saturation (SaO2 ) of 90% (CT90) [8], the number of SaO2 events that drop either 3% or 4% (ODI) [9,10,15],a measure of SaO2 variability (Δ index) [11,12], a central tendency measure [13], and a multilayer perceptron neural network [16]. However, in our review of the literature, we uncovered only two other studies that have attempted to convert oximetry to an apnea-hypopnea index (AHI) [14,16]. In the Magalang et al study, both Δ index and a composite measure of various oximetry indexes were found to have coefficients of determination of 0.60 and 0.70 respectively. The Marco et al study produced a more accurate predictive formula than Magalang et al., However, scoring of the polysomnograms did not appear to utilize the updated guidelines recommended by the American Academy of Sleep Medicine(AASM) [17].

Therefore, the goal of this study is to develop a new predictive model of AHI using Oximetry data by utilizing more recent pulse oximetry technology, a large sample size, and the 2007 AASM scoring guidelines to evaluate the gold standard attended polysomnograms. We believe that by taking into account these factors we will be able to develop a new predictive model that out performs all previous models. Our predictive model will focus on ODI since it appears to be the most sensitive and specific oximetry index [9,18], and is therefore an ideal target for conversion to AHI.


Patient Population

Approval for the study was granted by the institutional review board at Weill Cornell Medical College. 1467 subjects given attended polysomnograms with concurrent pulse oximetry at the Center for Sleep Medicine, Weill Cornell Medical were utilized for this retrospective analysis (see table 1 for demographic data). Most of these subjects were suspected of OSA, a minority were suspected of other sleep disorders including (but not limited to) periodic limb movements, narcolepsy, sleep walking, and REM behavior disorder. All subjects ≥18 years old studied between April 14, 2010 and February 7, 2012, who had not received PAP therapy and/or supplemental oxygen therapy on their polysomnogram were included in this analysis. Subjects were randomized by assigning each case a random number, ordering the numbers in an ascending fashion, and splitting the randomly ordered cases into a model development group (n=733), and a verification group (n=734).

No significant differences were found between the model development and validation groups on any variable listed above at a p<0.05 using independent t-tests.


Previously described standard techniques were employed on all-night attended sleep recordings using Grass Technologies Twin® digital polysomnographs with an integrated Nonin clip Oximetry (see below for additional details on Oximetry). Standard polysomnogram montage and digital filter settings recommended by the AASM were employed [19]. Respiratory effort was measured by Sleep sense® inductive plethysmography belts placed around the rib cage and abdomen. Airflow was determined by the Pro-Tech PTAF lite® pressure transducer on the baseline study. The nasal cannula for the pressure transducer was placed at the level of the upper lip in midline position. A continuous electrocardiogram recorded heart rate and rhythm. Respiratory events were classified according to AASM criteria: an apnea was defined as a decrease in peak nasal pressure of >90% of baseline, lasting at least 10 seconds. Hypopnea was defined by a decrease of >30% of the baseline nasal pressure, lasting at least 10 seconds and associated with a ≥4% drop in the oxyhemoglobin saturation. All records were reviewed by board certified sleep specialists and scored by registered polysomnographic technicians.

Pulse Oximetry

The oximeter used was the Nonin Xpod® model 3011 with an adult finger clip senor (Nonin 8000AA) utilizing Pure SAT® technology that automatically adjusts to provide pulse to pulse averaging of three seconds or faster (based on pulse rates 60 BPM and greater). ODI was calculated by the number of ≥4% drops in oxyhemoglobin saturation over total recording time.

Statistical Analysis

SPSS version 21 and R version 3.2.1 were used for statistical analysis. Linear, multivariate adaptive sp lines, segmented, and quadratic regression modeling was used to develop the predictive models of AHI using ODI. A log transformed (to address a non normal distribution of the residuals) quadratic regression model provided the best fit compared to the other models. Therefore, the results listed below are only based on the transformed quadratic model. The regression algorithm was developed with 733 subjects. Verification of the model was performed using a separate group of 734 subjects (see Table 1 for demographic information on the subject groups). Sensitivity and specificity are shown for AHI break points of ≥5/hr., ≥10/hr., and ≥15/hr. due to the frequent use of these threshold levels in clinical practice to determine the need for treatment of sleep apnea. In addition, an AHI threshold of ≥30/hr. is also shown to illustrate the role of our predictive model in identifying subjects with severe sleep apnea. Confidence intervals (CI) are all listed as 95%.

Table 1: Demographic information.

  Subject group (n=1467)
Model development group Model validation group
Mean±SD Count Mean±SD Count
Gender Female   288   273
Male   444   460
Age (Years) 48.47±16.17   49.8±16.81  
BMI (kg / m²) 28.20±6.90   27.94±6.56  
Epworth Score 8±5   8±5  
ODI Total (Events / h) 8.5±12.7   7.9±10.3  
AHI Total (Events / h) 14.7±19.6   14.00±17.3  
Avg O2 Sat Total (%) 94.5±6.4   94.5±6.4  
Avg O2 Sat Wake (%) 92.5±16.5   93.5±13.3  
Avg O2 Sat NREM (%) 92.0±15.7   93.1±12.3  
Avg O2 Sat REM (%) 87.3±25.5   89.7±64  
Average HR Total (BPM) 64±11   64±10  
Sleep Efficiency (% TST) 74.4±20.1   74.8±18.1  
Total Sleep Time (Min) 346±106   351±103  
Demographic information about the 1467 subjects Abbreviations: BMI: Body Mass Index; H: Hours; Avg: Average; 02 Sat: Oxygen Saturation; NREM: Non-Rapid Eye Movement Sleep; REM: Rapid Eye Movement; HR: Heart Rate; BPM: Beats Per Minute; TST: Total Sleep Time; Min: Minute No significant differences were found between the model development and validation groups on any variable listed above at a p<0.05 using independent t-tests



The coefficient of determination (r2 ) between the actual AHI and the predicted AHI (Pred AHI) was 0.80 (r=0.90), which was significant at a p<0.001.PredAHI determined a correct AHI ±5/hr. in 76% of subjects. The intra class correlation for single measures using an absolute agreement definition was 0.88 (CI 0.87-0.90). The subjects that had a Pred AHI greater than ±5/hr were significantly older t (732) = -5.311, p<0.001, had a higher AHI t(732)=-16.89, p<0.001, and a lower sleep efficiency t (732)=5.12, p<0.001.

The AUC was 0.97±0.005 (SE), CI 0.96-0.98 for an AHI of ≥5/ hr., 0.96±0.007 (SE), CI 0.94-0.97 for an AHI ≥10/hr., 0.96±0.007 (SE), CI 0.95-0.98 for an AHI of ≥15/hr., and 0.97±0.008 (SE), CI 0.96-0.99 for an AHI of ≥30/hr. (see Figure 1 for ROC curves, and Table 2 for other measures of test precision). The asymptotic significance level for AUC at all tested thresholds was p<0.001.

The center line of the plot represents the mean, the other two lines indicate ±2 SD. It shows data from 734 subjects (the validation set). The gold standard measure of AHI was derived from in-laboratory polysomnography scored according to AASM 2007 guidelines.


This analysis shows that an accurate prediction of AHI can be made using a regression formula derived from ODI, with areas under the ROC curve ranging from 0.96 for thresholds of ≥10 and ≥15/hr. to 0.97 for thresholds of ≥5 and ≥30/hr. This is better than most previously published comparisons of ODI to AHI [20,14,10]. Only one other study appears to outperform our model [9], however this study does not attempt to convert ODI to a predicted AHI. Moreover, our model was developed and compared to an AHI calculated using the AASM’s currently recommended scoring guidelines for respiratory events [19], and therefore is more applicable for use today. In comparison to the Magalang et al., models [14], which showed r2 ’sof 0.60, and 0.70 for predicting AHI with Δ index alone and a composite measure of oximetry respectively, our model outperformed these algorithms with an r2 of 0.80. The Marcos et al., [16] model slightly out performed our model at an AHI of 15/hr (93% vs. 91%). However, our model was more accurate at AHI’s of 5/hr. (84% vs. 91% and 10/hr. (87% vs. 90%). In addition, our model was very accurate at an AHI of 30/hr (Table 2).

Due to the fact that ODI cannot differentiate between central and obstructive events, we do not intend for this algorithm to replace traditional polysomnograms. However, this formula can be useful in diagnosing patients that have had either a home or inlaboratory sleep study, but the flow sensor data is unavailable. It is not uncommon in clinical practice to have patients remove the flow sensor because of discomfort; in these cases a predicted AHI can be calculated from the ODI, and the respiratory effort activity can be viewed to gain an estimate of central vs. obstructive apnea. This formula can also be used with pulse oximeters to convert nighttime oximetry data to AHI in order to determine if additional testing or treatment for sleep apnea is warranted.

As mentioned in the introduction, since the 1990’s studies have shown the value of using oximetry in assessing apnea [8- 14]. However, the use of oximetry indexes for clinical decision making in sleep disordered breathing remains uncommon. The ability to convert oximetry data to an AHI allows a more readily understandable metric that sleep specialists and most non-sleep specialists alike can interpret.

As seen in Figure 2, Pred AHI tends to underestimate AHI in the severe range. This is likely due to the fact that as the actual AHI becomes very high, the individual apneas tend to be shorter, resulting in fewer respiratory events resulting in ≥4% drop in blood oxygen saturation (e.g. If an average apnea length is 15 sec. the maximum AHI is 120/hr. vs. 90/hr. for an average apnea length of 20 sec.). However, the accuracy (Table 2) of Pred AHI at the ≥30/h AHI threshold is high at 95%. Differentiating whether a true AHI is 50/hr. vs. 90/hr. is not as important as determining if an AHI is in the severe range, therefore we do not believe this discrepancy will significantly affect clinical decisions to treat sleep apnea based on our formula. Another limitation of this study is the use of a single testing site. However, the heterogeneity of our patient population in New York City moderates this concern to some degree. Moreover, the use of a large sample size in both developing and validating our model gives confidence in its accuracy.

In summary, the algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea. Our goal is to pair our formula with a commercially available pulse oximeter in order to provide a low cost screening tool for sleep apnea to determine the need for additional evaluation and/ or treatment of sleep disordered breathing. We believe this instrument will be useful for trades such as the transportation industry, which is in need of a low cost and accurate way to asses for sleep apnea, or for patients living in rural or impoverished areas where traditional attended polysomnography may not be available or may be too costly.

Table 2: Stratified results for predicting sleep apnea using the PredAHI algorithm.

  Sensitivity Specificity PPV NPV Accuracy
AHI >=5 h .90 .92 .95 .85 .91
AHI >=10 h .86 .94 .91 .90 .90
AHI >=15 h .82 .96 .89 .92 .91
AHI >=30 h .76 .98 .85 .96 .95
Abbreviations: PPV: Positive Predictive Value; NPV: Negative Predictive Value



We would like to thank Nahal Mansoori for her help with data management for this study.

Dr. Matthew Ebben takes responsibility for the content of the manuscript, including the data and analysis.

Competing interests

The Cornell University technology commercialization office has licensed the formula described in the paper for Matthew R. Ebben and Ana C. Krieger. Matthew R. Ebben also works as a consultant for Apnostics, the company that has licensed the algorithm described in the paper from Cornell University.


1. Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993; 328: 1230-1235.

2. Sin DD, Fitzgerald F, Parker JD, Newton G, Floras JS, Bradley TD. Risk factors for central and obstructive sleep apnea in 450 men and women with congestive heart failure. American journal of respiratory and critical care medicine. 1999; 160: 1101-1106.

3. Yaggi HK, Concato J, Kernan WN, Lichtman JH, Brass LM, Mohsenin V. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med. 2005; 353: 2034-2041.

4. Kessler R, Chaouat A, Weitzenblum E, Oswald M, Ehrhart M, Apprill M, et al. Pulmonary hypertension in the obstructive sleep apnoea syndrome: prevalence, causes and therapeutic consequences. Eur Respir J. 1996; 9: 787-794.

5. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000; 342: 1378-1384.

6. Nieto FJ, Peppard PE, Young T, Finn L, Hla KM, Farré R. Sleepdisordered breathing and cancer mortality: results from the Wisconsin Sleep Cohort Study. Am J Respir Crit Care Med. 2012; 186: 190-194.

7. Punjabi NM, Caffo BS, Goodwin JL, Gottlieb DJ, Newman AB, O’Connor GT, et al. Sleep-disordered breathing and mortality: a prospective cohort study. PLoS Med. 2009; 6: 1000132.

8. Gyulay S, Olson LG, Hensley MJ, King MT, Allen KM, Saunders NA. A comparison of clinical assessment and home oximetry in the diagnosis of obstructive sleep apnea. Am Rev Respir Dis. 1993; 147: 50-53.

9. Vázquez JC, Tsai WH, Flemons WW, Masuda A, Brant R, Hajduk E, et al. Automated analysis of digital oximetry in the diagnosis of obstructive sleep apnoea. Thorax. 2000; 55: 302-307.

10. Chiner E, Signes-Costa J, Arriero JM, Marco J, Fuentes I, Sergado A. Nocturnal oximetry for the diagnosis of the sleep apnoea hypopnoea syndrome: a method to reduce the number of polysomnographies? Thorax. 1999; 54: 968-971.

11. Lévy P, Pépin JL, Deschaux-Blanc C, Paramelle B, Brambilla C. Accuracy of oximetry for detection of respiratory disturbances in sleep apnea syndrome. Chest. 1996; 109: 395-399.

12. Olson LG, Ambrogetti A, Gyulay SG. Prediction of sleep-disordered breathing by unattended overnight oximetry. J Sleep Res. 1999; 8: 51- 55.

13. Alvarez D, Hornero R, Garcia M, Del Campo F, Zamarron C. Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure. Artif Intell Med. 2007; 41: 13-24.

14. Magalang UJ, Dmochowski J, Veeramachaneni S, Draw A, Mador MJ, El Solh A, et al. Prediction of the apnea-hypopnea index from overnight pulse oximetry. Chest. 2003; 124: 1694-1701.

15. Chung F, Liao P, Elsaid H, Islam S, Shapiro CM, Sun Y. Oxygen desaturation index from nocturnal oximetry: a sensitive and specific tool to detect sleep-disordered breathing in surgical patients. Anesth Analg. 2012; 114: 993-1000.

16. Marcos JV, Hornero R, Álvarez D, Aboy M, Del Campo F. Automated prediction of the apnea-hypopnea index from nocturnal oximetry recordings. IEEE Trans Biomed Eng. 2012; 59: 141-149.

17. AASM. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. 2007.

18. Lin CL, Yeh C, Yen CW, Hsu WH, Hang LW. Comparison of the indices of oxyhemoglobin saturation by pulse oximetry in obstructive sleep apnea hypopnea syndrome. Chest. 2009; 135: 86-93.

19. Iber C. American Academy of Sleep Medicine. The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specifications. American Academy of Sleep Medicine. 2007.

20. Golpe R, Jiménez A, Carpizo R, Cifrian JM. Utility of home oximetry as a screening test for patients with moderate to severe symptoms of obstructive sleep apnea. Sleep. 1999; 22: 932-937.

Received : 06 Jan 2016
Accepted : 02 Feb 2016
Published : 04 Feb 2016
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
Annals of Biometrics and Biostatistics
ISSN : 2374-0116
Launched : 2013
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
TEST Journal of Dentistry
ISSN : 1234-5678
Launched : 2014
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X